eBay:iPads:: startprice.log10 regression:: ebayipads_finmdl_bid0_sp
bdanalytics
Date: (Fri) Oct 16, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 -> Rank 288 / 1884 0.85514 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
assctxt: select_terms: [1] “condit” “use” “scratch” “new” “good” “ipad” “screen” “great”
[9] “work” “excel” “like” “box” “function” “item” “fulli” “minor” [17] “cosmet” “crack” “mint” “wear”
assoc_terms: [1] “bare” “sign” “light” “back” “hous” “tab” “dent”
[8] “brand” “open” “mini” “appl” “air” “wifi” “affect”
[15] “protector” “shape” “perfect” “order” “button” “origin” “retail”
[22] “seal” “includ” “100” “may” “show” “overal” “bodi”
[29] “phone” “will” “damag” “near” “top” “normal” “tear”
[36] “expect” “minim”
glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6665 (97.3037 pct) All.Interact.X.glmnet: fit_RMSE=88.40723; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctriPadAir:D.npnct01.log=79.67748; D.TfIdf.sum.stem.stop.Ratio=79.08192; prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)=78.24020; prdline.my.fctriPad 3+:color.fctrSpace Gray=77.05886; prdline.my.fctriPadmini 2+:storage.fctrUnknown=75.68145; prdline.my.fctrUnknown:.clusterid.fctr3=74.23727;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.72974
newobs_tbl=[N=682, Y=116]; submit_filename=assctxt_Final_rf_submit
OOB_conf_mtrx=[YN=125, NY=43]=168; max.Accuracy.OOB=0.8101695; max.auc.OOB=???;
opt.prob.threshold.OOB=0.6
biddable=100.00; startprice.diff=51.04; idseq.my=29.51;
startprice.diff:biddable=28.70
prdline.my.fctriPadmini:idseq.my=6.89
Highest max.auc.OOB=???; for model: ctgry2: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6559 (96.7556 pct) All.Interact.X.glmnet: next: All.X.glmnet fit_RMSE=88.80010; prdl.my.descr.fctr:storage.fctr 100.00 prdl.my.descr.fctr:condition.fctr 93.96 prdl.my.descr.fctr:D.npnct01.log 89.94 D.TfIdf.sum.stem.stop.Ratio 75.90 prdl.my.descr.fctr:color.fctr 72.43 prdl.my.descr.fctr:.clusterid.fctr7 63.97 prdl.my.descr.fctr:D.npnct08.log 63.46 prdl.my.descr.fctr 63.05 prdl.my.descr.fctr:D.TfIdf.sum.stem.stop.Ratio 62.91 prdl.my.descr.fctr:D.npnct16.log 62.39
Ensemble.glmnet: Leaderboard: 0.80480
newobs_tbl=[N=473, Y=325]; submit_filename=ctgry2_Final_glmnet_submit
OOB_conf_mtrx=[YN=79, NY=101]=180;
max.Accuracy.OOB=0.7977528; max.auc.OOB=0.8554068; opt.prob.threshold.OOB=0.4
Highest max.auc.OOB=0.8587215; for model:All.X.no.rnorm.rf
biddable 100.000
startprice.diff 71.793
idseq.my 43.511
ensemble: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6570 (96.9282 pct) Final.glment: min.RMSE.fit=31.45801 Ensemble.glmnet: min.RMSE.fit=30.67172 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 75.381 All.X.glmnet: min.RMSE.fit=88.98066 prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 92.16 condition.fctr 79.01 prdl.my.descr.fctr:.clusterid.fctr5 69.91 D.npnct16.log 61.70 color.fctrWhite 59.42 D.npnct01.log 55.07 cellular.fctr1 53.35 D.terms.n.post.stop 52.92
Ensemble.glmnet: Leaderboard: 0.73183
newobs_tbl=[N=557, Y=241]; submit_filename=ensemble_Final_glmnet_submit
OOB_conf_mtrx=[YN=75, NY=60]=135;
max.Accuracy.OOB=0.8483146; max.auc.OOB=0.9187365;
opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.000000
sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 98.873608
Highest max.auc.OOB=0.9180131; for model:All.X.no.rnorm.rf
startprice.diff 100.000 biddable 95.318 idseq.my 33.365
ncv7: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6570 (96.9282 pct) Final.glment: min.RMSE.fit=31.45704 Ensemble.glmnet: min.RMSE.fit=29.93289 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 82.878 startprice.predict.Low.cor.X.lm 42.664
All.Interact.X.glmnet: min.RMSE.fit=87.30181 prdl.my.descr.fctr:D.npnct01.log 100.00 prdl.my.descr.fctr:condition.fctr 99.98 prdl.my.descr.fctr:storage.fctr 96.26 prdl.my.descr.fctriPadAir 79.65 prdl.my.descr.fctr:color.fctr 79.30 prdl.my.descr.fctr:D.TfIdf.sum.stem.stop.Ratio 74.90 D.TfIdf.sum.stem.stop.Ratio 74.77 prdl.my.descr.fctr:D.npnct08.log 67.38 prdl.my.descr.fctr:D.npnct01.log 67.38
Ensemble.glmnet: Leaderboard: not submitted -> lower max.auc.OOB of "Ensemble submission"
newobs_tbl=[N=561, Y=237]; submit_filename=ncv7_Final_glmnet_submit
OOB_conf_mtrx=[YN=79, NY=55]=134;
max.Accuracy.OOB=0.8494382; max.auc.OOB=0.9130918; opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.000 sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 99.348
Highest max.auc.OOB=0.9117720; for model:All.X.no.rnorm.rf
startprice.diff 100.000 biddable 96.383 idseq.my 32.634
gbm w/startprice.unit9: Final.glment: min.RMSE.fit=30.32782 Ensemble.glmnet: min.RMSE.fit=29.62348 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 73.521 startprice.predict.All.Interact.X.bayesglm 29.675 startprice.predict.Max.cor.Y.lm 28.405
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
Ensemble.glmnet: Leaderboard: not submitted -> lower max.auc.OOB of "Ensemble submission"
newobs_tbl=[N=579, Y=219]; submit_filename=gbm_Final_glmnet_submit
OOB_conf_mtrx=[YN=85, NY=54]=139;
max.Accuracy.OOB=0.8438202; max.auc.OOB=0.9127314; opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.0000 sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 98.7937
Highest max.auc.OOB=0.9167568; for model:All.Interact.X.gbm biddable 100.0000 startprice.diff 96.2076 startprice.diff:biddable 23.2114 idseq.my 7.8098
mdlsel: Final.glment: min.RMSE.fit=30.47114 (higher than gbm w/startprice.unit9) Ensemble.glmnet: min.RMSE.fit=29.49418 startprice.predict.All.Interact.X.no.rnorm.rf 100.000000 startprice.predict.All.X.no.rnorm.rf 71.213880 startprice.predict.All.X.bayesglm 24.166084
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
mdlsel(startprice.log): Final.Ensemble.rf: min.RMSE.fit=0.4563772 Ensemble.rf: min.RMSE.fit=0.4283013 startprice.log.predict.All.Interact.X.no.rnorm.rf 100.0000000 startprice.log.predict.All.X.no.rnorm.rf 58.0967582 startprice.log.predict.All.Interact.X.gbm 6.7197148
All.X.no.rnorm.rf: min.RMSE.fit=1.4967021biddable 100.00000000 idseq.my 98.00292371 startprice.unit9 34.31130220 prdl.my.descr.fctr 18.10984741 D.ratio.sum.TfIdf.nwrds 15.23549621 color.fctrUnknown 14.05520993 D.TfIdf.sum.stem.stop.Ratio 13.00884673 D.ratio.nstopwrds.nwrds 10.51165302
All.X.gbm: Leaderboard: 0.75430
newobs_tbl=[N=582, Y=216]; submit_filename=mdlsel_Final_gbm_submit
OOB_conf_mtrx=[YN=58, NY=65]=123;
max.Accuracy.OOB=0.8617978; max.auc.OOB=0.9367161;
opt.prob.threshold.OOB=0.5startprice.diff 100.0000000 100.00000000 biddable 66.6475055 65.40764971 idseq.my 1.8632456 4.55963698
splogdiff: All.X.gbm: Leaderboard: 0.70111 newobs_tbl=[N=553, Y=245]; submit_filename=splogdiff_Final_gbm_submit OOB_conf_mtrx=[YN=35, NY=101]=136; max.Accuracy.OOB=0.8471910; max.auc.OOB=0.9388912; opt.prob.threshold.OOB=0.3 startprice.log.diff 100.0000000 100.0000000 biddable 86.8563123 88.0261866 idseq.my 8.3580281 2.9054298
nofrcdups: All.X.gbm: Leaderboard: ???/0.67225 newobs_tbl=[N=543, Y=255]; submit_filename=nofrcdups_Final_gbm_submit OOB_conf_mtrx=[YN=36, NY=101]=137; opt.prob.threshold.OOB=0.3 max.Accuracy.OOB=0.0.8460674; max.auc.OOB=0.9388582; startprice.log.diff 100.00000000 93.3716491 biddable 83.57786348 100.0000000 idseq.my 11.54696712 1.1240259
nofrcdups w/ glb_sel_mdl_id=All.X.no.rnorm.rf: All.X.no.rnorm.rf: Leaderboard: ???/0.57475 -> ???/0.59937 with force dups newobs_tbl=[N=630, Y=168]; newobs_range_outliers=798; submit_filename=nofrcdups_Final_rf_submit OOB_conf_mtrx=[YN=70, NY=62]=132; OOBobs_range_outliers=27 opt.prob.threshold.OOB=0.5 max.Accuracy.OOB=0.8516854; max.auc.OOB=0.9335308; startprice.log.diff 100.00 biddable 87.61 idseq.my 26.79
Forum Ideas: I then focused on feature engineering, each new variable brought its own little improvement so in the end i just kept adding new ones and let the models do their thing. Here are some i used: model (productline:storage:condition), isNew, model2 (product:isNew), 50 common words from descr, descrLength, capsFactor (% of caps in description), number of cheaper items of same model2, number of dearer items of same model2, priceFactor (vs. mean of price for model), priceFactor2 (vs. mean of price for model2), bigID (if ID> 11000 because there seems to be a huge drop in sales after some time), timeline (year of product launch, reasoning is you want to spend less money on older products).
Get the median startprice for each level of productline and condition. Take the difference from startprice as a new variable. I find median works much better than the mean since startprice is not normally distributed. I also created another binary variable on whether this difference is positive or negative.
Square root startprice
scale and center all the variables except sold, including the dummies.
Prediction Accuracy Enhancement Options:
- Not repeatable experiments:
- gbm & ensembles that include gbm
- rf that includes .rnorm ??? (now .rnorm has a set seed)
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/confusionMatrix.R")
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/ggplot.R")
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "ebayipads_finmdl_bid0_sp_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_obs_drop_condition <- #NULL
"(UniqueID %in% c(NULL
, 11234 #sold=0; 2 other dups(10306, 11503) are sold=1
, 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
)) | (productline %in% c('iPad 5', 'iPad mini Retina')) |
(biddable != 0)"
#parse(text=glb_obs_drop_condition)
glb_obs_repartition_train_condition <- #NULL
"!is.na(sold) & (sold == 1)"
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "startprice"
# for classification, the response variable has to be a factor
glb_rsp_var <- "startprice.log10" # "sold.fctr" # glb_rsp_var_raw
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) { # NULL
# return(raw ^ 0.5)
# return(log(1 + raw))
return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00))## [1] NA -Inf 0.4756712 2.4479329 3.0000000glb_map_rsp_var_to_raw <- function(var) { # NULL #
# return(var ^ 2.0)
# return(exp(var) - 1)
return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))## [1] NA 0.00 2.99 280.50 1000.00if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # mdl_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
# User-specified exclusions
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- c(NULL
, "description", "productline"
, "sold", "startprice.log10.cut.fctr"
### bid0_sp
# List feats that are linear combinations (alias in glm)
, "D.terms.post.stem.n.log", "D.weight.sum"
#, "prdl.descr.my.fctriPad4#1:.clusterid.fctr3" This does not work
# if RFE is rated lower than Low.cor, list feats that are in RFE & not in Low.cor
# min.RMSE.fit(RFE.X.glmnet)=0.1138888
# D.chrs.n.log 61.12483
# D.chrs.uppr.n.log 61.12483
# D.ratio.wrds.stop.n.wrds.n 61.12483
# D.terms.post.stop.n.log 61.12483
# D.weight.post.stem.sum 61.12483
# D.wrds.n.log 61.12483
# D.wrds.stop.n.log 61.12483
# D.wrds.unq.n.log 61.12483
#, "startprice.dcm2.is9" # min.RMSE.fit(RFE.X.glmnet)=0.1141991 (up)
, "D.wrds.stop.n.log" # min.RMSE.fit(RFE.X.glmnet)=0.1131232
###
)
glb_id_var <- c("UniqueID")
glb_category_var <- "prdl.descr.my.fctr" # "productline" # NULL
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1)
# glb_derive_lst[["prdline.my"]] <- list(
# mapfn=function(productline) { return(productline) }
# , args=c("productline"))
glb_derive_lst[["startprice.log10.cut.fctr"]] <- list(
mapfn=function(startprice.log10) { return(cut(startprice.log10, 3)) }
, args=c("startprice.log10"))
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# This is here because it does not work with txt_map_filename
mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|"), " ",
mod_raw)
# This should go into txt_map_filename
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
# Don't parse for "." because of ".com"; use customized gsub for that text
mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 128 gb ", " 128gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16G, ", " 16GB, ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16 gig ", " 16gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16 gb ", " 16gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bAccounts\\b", "Account", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bactivated\\b", "activate", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\baffects\\b", "affect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bApple\\'s", "Apple", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(665), txt_var]; mod_raw
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\bblacked\\b", "black", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bblemish\\b", "blemishes", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" brokenCharger ", " broken Charger ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcord", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcables\\b", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" care\\.The ", " care\\. The ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(cared|careful|CAREFUL)\\b", "care", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(cases|casing)\\b", "case", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(88,187,280,1040,1098), txt_var]; mod_raw
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bchargers\\b", "charger", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bchips\\b", "chip", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bcleanly\\b", "clean", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(C|c)olor(.*)s\\b", "\\1olor", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(280,1411), txt_var]; mod_raw
mod_raw <- gsub("\\bcompletely\\b", "complete", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(42,502,676), txt_var]; mod_raw
mod_raw <- gsub("\\b(conditon|condtion|contidion|conditions)\\b", "condition", mod_raw,
ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(234), txt_var]; mod_raw
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub("\\bCONNECTED\\b", "CONNECT", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bconnects\\b", "connect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(195, 379, 437), txt_var]; mod_raw
mod_raw <- gsub("\\bCosmetics\\b", "Cosmetic", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(D|d)amaged\\b", "\\1amage", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(116, 1360), txt_var]; mod_raw
mod_raw <- gsub("\\bDays\\b", "Day", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bdefect(ive)*\\b", "defects", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(1403), txt_var]; mod_raw
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(D|d)ented\\b", "\\1ent", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(ding|dinged)\\b", "dings", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(drop|drops)\\b", "dropped", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(E|e)dge\\b", "\\1dges", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fine.Its ", " fine. Its ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfix\\b", "fixed", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bflaws\\b", "flaw", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bflawlessly\\b", "flawless", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" functioanlity", " functionality", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfunction(ing|ality)\\b", "functional", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" functional\\.Very little ", " functional\\. Very little ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\b(guarantee|guarantees)\\b", "guaranteed", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\ba handful of times\\b", "sparingly", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bhardly any\\b", "no", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bhardly ever used\\b", "sparingly used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCLOUD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("----I cloud ", " ----Icloud ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" IMEINo ", " IMEI No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bincluding\\b", "included", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^I pad ", "Ipad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(lock|locks)\\b", "locked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\blots\\b", "lot", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\bmarkings\\b", "marks", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(200, 1301), txt_var]; mod_raw
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bmonth\\b", "months", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(29, 38, 194, 511, 789, 819), txt_var]; mod_raw
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bnoted\\b", "note", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("otter box", "otterbox", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bpackage\\b", "packaging", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPACKAGE\\b", "PACKAGing", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(360, 1142), txt_var]; mod_raw
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bPhysically\\b", "Physical", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(picture|pictured)\\b", "pictures", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPICTURE\\b", "PICTUREs", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(184, 892), txt_var]; mod_raw
mod_raw <- gsub("\\b[P|p]ower(ed|ing|s)\\b", "\\1ower", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" pre- owned ", " used ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bprevious\\b", "previously", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bpreviously (owned|used)\\b", "used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bproblem\\b", "problems", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bprotected\\b", "protector", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bprotection\\b", "protector", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPROTECTION\\b", "PROTECTOR", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" lightening ", " lightning ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bminis\\b", "mini", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(scratchs|scratching)\\b", "scratches", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(S|s)creens\\b", "\\1creen", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(83, 489), txt_var]; mod_raw
mod_raw <- gsub("\\bset\\b", "settings", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bshowing\\b", "shows", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bshuts\\b", "shut", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bSlightly\\b", "slight", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bspace (grey|gray)", "spacegray", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bstoring", "store", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPAsdotCOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(tear|TEAR)(s|S)\\b", "\\1", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(376), txt_var]; mod_raw
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bTURN\\b", "TURNS", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" UnlockedCracked ", " Unlocked Cracked ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bUNUSABLE\\b", "UNUSED", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(update|updates)\\b", "updated", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bupgrade\\b", "upgraded", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bwears\\b", "\\wear", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(167, 272), txt_var]; mod_raw
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
# Text Processing Step: custom modifications not present in txt_munge
glb_txt_vars <- c("descr.my") # NULL #
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: removePunctuation (use custom transformer to replace with space ???)
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words; Check stemming of "significant" words - any stopped words that should be stemmed with them ?
if (!is.null(glb_txt_vars)) {
require(tm)
glb_txt_stop_words[["descr.my"]] <- sort(c(NULL
, setdiff(removePunctuation(stopwords("english")), "no")
,"ac"
# cor.y.train == NA
,unlist(strsplit(paste(c(NULL
,"128gb,1st,32gb,3g,64gb,90,acceptable,activation,amount,average,bad,buttons,buy,came,camera,can,care,carrier"
#,casing
,"certified,charge,charging,cleaned,clear,come,components,contain,corner,correctly,covered,customer,earbuds"
,"engraved,engraving,engravement" # somehow didn't show up in the cor.y.train == NA list
,"entire,except,fair,features,feel,fine,generation,get,gift,got,heavily,heavy,however,imei,include,inspected,invisible,invisibleshield"
,"ipad,ipads"
,"issues"
#,items,
,"keyboard,lightning,listing,little,looks,lower"
,"manufacture,manufacturer"# somehow didn't show up in the cor.y.train == NA list
,"meaning,model,near,need,needs,nicks,opened,operational,otherwise"
,"person,personal"# somehow didn't show up in the cor.y.train == NA list
,"phone,photos,pics,plastic,port,professionally"
,"purchased,purchasing"# somehow didn't show up in the cor.y.train == NA list
,"quality,questions,read,ready"
,"receive,received"# somehow didn't show up in the cor.y.train == NA list
,"removed,replaced,retail,return,returns,runs"
#,scratch,
,"scuffing,sealed,sell,seller,selling,shape,ship,shown,silver,since,sold,sound,spacegray,stock,sync,tablet,taken,technician,tests,third,time,touch,units,unlocked,week,wifi,without"
,"wrap" # somehow didn't show up in the cor.y.train == NA list
,"zagg"
), collapse=",")
, "[,]")) #err.abs.fit.sum=26.869473 w/o items,scratch
# cor.y.abs is low
#,"always","comes","grade","moderate","protector"
))
} ## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydsp_obs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glb_allobs_df[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glb_category_var, "storage", txt_var)]
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glb_allobs_df$.lcn == "Fit", term_row_df$pos], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^p", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glb_allobs_df[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glb_category_var, txt_var)]
#which(glb_allobs_df$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glb_allobs_df$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glb_txt_synonyms <- list()
glb_txt_synonyms[["descr.my"]] <- #NULL #: default
list(NULL
, list(word="cabl", syns=c("cabl", "cord"))#err.abs.fit.sum=26.863220
# , list(word="charger", syns=c("charg", "charger"))
# , list(word="come", syns=c("came", "come"))
# , list(word="dent", syns=c("dent", "ding"))
# , list(word="damag", syns=c(#"bad", "blemish", "broken", "crack",
# #defect has +ve cor, others have -ve cor
# "damag", "dent", "ding",
# #"scratch", "scuff", "tear", "wear",
# NULL))
# # combining damag with defect & dent results in higher err.abs.fit.sum=26.885899
# # combining defect with dent in higher err.abs.fit.sum=26.894976
# , list(word="defect", syns=c(#"bad", "blemish", "broken", "crack",
# "defect", "dent", #"ding", ding has -ve cor, others have +ve cor
# #"scratch", "scuff", "tear", "wear",
# NULL))
#, list(word="new", syns=c("brand")) ???
# , list(word="scuff", syns=c("scuf", "scuff"))
# , list(word="show", syns=c("show", "shown"))
# , list(word="tablet", syns=c("tab", "tablet"))
)
if (length(glb_txt_synonyms) > 0) names(glb_txt_synonyms) <- glb_txt_vars
# Text Processing Step: filterTerms
if (!is.null(glb_txt_vars)) {
require(tm)
# options include: weightTf, myweightTflog1p, myweightTfsqrt, weightTfIdf, weightBM25
glb_txt_terms_control <- list(weighting=weightTfIdf
# termFreq selection criteria across obs: default: list(global=c(1, Inf))
, bounds=list(global=c(3, Inf))
# termFreq selection criteria for each obs: default: list(local=c(1, Inf))
#, bounds=list(local=c(2, Inf))
# default: c(3, Inf)
, wordLengths = c(2, Inf)
)
}
glb_txt_cor_var <- "startprice.log10.cut.fctr" # glb_rsp_var
# select one from c("union.top.val.cor", "top.cor", default: "top.val", "sparse")
glb_txt_terms_filter <- "top.val"
glb_txt_top_n <- c(20) # 50 in non_sp
names(glb_txt_top_n) <- glb_txt_vars
# Text Processing Step: extractAssoc
glb_txt_assoc_cor <- c(0.4) # 0.2 in non_sp
names(glb_txt_assoc_cor) <- glb_txt_vars
# Text Processing Step: extractPatterns (ngrams)
# Potential Enhancements
# "Seller refurbished" -> D.P.refurbished.seller ?
# "Like new" -> D.P.new.like ?
# "No scratches" -> D.P.scratch.no ?
glb_important_terms <- list()
# Remember to use stemmed terms
# Have to set it even if it is not used
glb_sprs_thresholds <- c(0.950) # Generates 8 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
glb_price_vars <- c("startprice") # NULL or c("<price_var>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE # default:FALSE
glb_cluster.seed <- 189 # or any integer
# c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glb_cluster_entropy_var <- "sold" #"startprice.log10.cut.fctr"
glb_exclude_cluster_vars_as_features <- TRUE # default FALSE
glb_interaction_only_feats_lst <- list()
glb_interaction_only_feats_lst[["carrier.fctr"]] <- "cellular.fctr"
glb_nzv_freqCut <- 19 # 19 is caret default
glb_nzv_uniqueCut <- 4 # 10 is caret default
# outliers identified by car::outlierTest
glb_obsfit_outliers <- c(NULL # default: NULL
# biddable == 0 & 1; err.abs.fit.sum=423.55172
# # outliers
# , 10813 # next 665 w/ rstudent=-5.091080; biddable=3.263257; err.abs.fit.sum=418.598755
# , 10666 # next 1727 w/ rstudent=-5.163517; biddable=4.293465; err.abs.fit.sum=414.093609
# , 11736 # next 780 w/ rstudent=-5.181343; biddable=5.670483; err.abs.fit.sum=401.817992
# # old biddable importance above this
# , 10781 # next 1323 w/ rstudent=-5.151062; biddable=13.30602; err.abs.fit.sum=396.393721
# #, 10091 # next 91 w/ rstudent=-4.444452; biddable=; err.abs.fit.sum=402.673715 (up)
# #, 10166 # next 560 w/ rstudent=-5.006795; biddable=; err.abs.fit.sum=401.759324 (up)
# #, 10281 # next 281 w/ rstudent=-4.245087; biddable=; err.abs.fit.sum=401.316926 (up)
# #, 10285 # next 285 w/ rstudent=-4.483190; biddable=; err.abs.fit.sum=402.608936 (up)
# #, 10446 # next 445 w/ rstudent=-4.663418; biddable=; err.abs.fit.sum=403.074523 (up)
# #, 10542 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10543 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10561 # next 542 w/ rstudent=-4.736154; biddable=; err.abs.fit.sum=401.56198 (up)
# #, 10631 # next 166 w/ rstudent=-5.073048; biddable=; err.abs.fit.sum=401.556788 (up)
# #, 11330 # next 630 w/ rstudent=-5.117659; biddable=; err.abs.fit.sum=401.732597 (up)
# , 10091, 10166, 10281, 10285, 10446, 10542, 10543, 10561, 10631, 11330
# # biddable=18.93923; err.abs.fit.sum=359.388769
# , 10330 #biddable=19.06084; err.abs.fit.sum=355.895702
# , 10402 #biddable= 0.0 ; err.abs.fit.sum=351.315181
# , 10438 #biddable= 0.0 ; err.abs.fit.sum=347.821527
# , 10624 #biddable= 0.0 ; err.abs.fit.sum=343.724904
# , 10659 #biddable= 0.0 ; err.abs.fit.sum=331.873603
# , 11323 #biddable=10.45901; err.abs.fit.sum=324.929562
# , 11422 #biddable= 0.0 ; err.abs.fit.sum=334.839805 (up)
# biddable == 0; err.abs.fit.sum=26.713317
, 11448 # outliers; next is 858 w/ rstudent=-5.855132; err.abs.fit.sum=24.212800
, 11583 # outliers; next is 856 w/ rstudent=-4.792849; err.abs.fit.sum=22.164035
, 11581 # outliers; next is 743 w/ rstudent=-4.005054; err.abs.fit.sum=18.842901
, 10837 # outliers; next is 336 w/ rstudent=-5.279215; err.abs.fit.sum=18.124560
, 11442 # outliers; next is 904 w/ rstudent=-4.474844; err.abs.fit.sum=15.533211
, 11697 # outliers; next is 874 w/ rstudent=-3.678664; err.abs.fit.sum=13.829375
, 10799 # .hatvalues == 1; total 8; iPadmini#1; err.abs.fit.sum=13.807283
#, 10017 # .hatvalues == 1; total 7; iPad3#1; err.abs.fit.sum=14.620782 (up)
#, 10027, 10859 # .hatvalues == 1; total 7; iPad1#1; err.abs.fit.sum=14.570246 (up)
, 10332 # .hatvalues == 1; total 7; iPad4#1; err.abs.fit.sum=13.706467
, 11759 # .hatvalues == 1; total 6; iPadAir2#1; err.abs.fit.sum=13.643043
, 10675 # .hatvalues == 1; total 5; iPadAir#1; err.abs.fit.sum=13.623787
#, 11119 # .hatvalues == 1; total 4; iPadmini3#1; err.abs.fit.sum=NA
, 10017, 10027, 10859 # .hatvalues == 1; total 1; iPad3#1 & iPad1#1; err.abs.fit.sum=13.438903
# biddable == 1; err.abs.fit.sum=361.78243
# , 10813 # outliers; next is 665 w/ rstudent=-5.021180; err.abs.fit.sum=356.83424
# , 10666 # outliers; next is 808 w/ rstudent=-4.764126; err.abs.fit.sum=352.46437
# , 11736 # outliers; next is 665 w/ rstudent=-4.614022; err.abs.fit.sum=348.59977
# , 10542 # outliers; next is 665 w/ rstudent=-4.654923; err.abs.fit.sum=344.18546
# , 11330 # outliers; next is 327 w/ rstudent=-4.628972; err.abs.fit.sum=336.12636
# , 10561 # outliers; next is 56 w/ rstudent=-4.612970; err.abs.fit.sum=329.50309
# , 10166 # outliers; next is 318 w/ rstudent=-4.717238; err.abs.fit.sum=318.50562
# , 10543 # outliers; next is 464 w/ rstudent=-4.811116; err.abs.fit.sum=314.32801
# , 10285 # outliers; next is 21 w/ rstudent=-4.850822; err.abs.fit.sum=310.19008
# #, 10091 # outliers; next is 464 w/ rstudent=-4.941448; err.abs.fit.sum=312.94069 (up)
# #, 10781 # outliers; next is 250 w/ rstudent=-4.793502; err.abs.fit.sum=313.03867 (up)
# , 10446 # outliers; next is 371 w/ rstudent=-4.787578; err.abs.fit.sum=307.15681
# , 10631 # outliers; next is 165 w/ rstudent=-4.130356; err.abs.fit.sum=303.34549
# #, 10330 # outliers; next is 217 w/ rstudent=-4.067684; err.abs.fit.sum=312.75121 (up)
# #, 10402 # outliers; next is 388 w/ rstudent=-4.067684; err.abs.fit.sum=311.84516 (up)
# #, 10659 # outliers; next is 128 w/ rstudent=-3.982911; err.abs.fit.sum=311.84516 (up)
# , 10091, 10781, 10330, 10402, 10659#, 10281 outliers; err.abs.fit.sum=282.381827; iPad4#0=13.806011; iPad4#1=7.799398
# #, 10281 # outliers; next is NA w/ rstudent=NA; err.abs.fit.sum=287.147331 (up); iPad4#0=14.372770; iPad4#1=4.591408
# #, 10624 # outliers; ignored along with 10281 err.abs.fit.sum=289.116467 (up); iPad4#0=; iPad4#1=
# #, 10624 # outliers; ignored w/o 10281 err.abs.fit.sum=286.415040 (up); iPad4#0=; iPad4#1=
# #, 10636 # hatvalues==1; next is 11652; err.abs.fit.sum=290.50254 (up)
# , 11652 # hatvalues==1; next is 10636; err.abs.fit.sum=282.183867
# #err.abs.fit.sum=282.227249
)
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#print(outliers <- car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel))
#print(outliers_df <- data.frame(.Bonf.p=outliers$bonf.p))
#model_diags_df <- cbind(glb_fitobs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[["RFE.X.glm"]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[["RFE.X.glm"]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)))
#print(subset(model_diags_df, is.na(.dffits)))
#print(subset(model_diags_df, .hatvalues == 1))
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glb_fitobs_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#indep_vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(glb_sel_mdl_id, ".importance"))), glb_featsimp_df))); indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(model_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glb_category_var] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#table(glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), c(glb_id_var, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glb_category_var)
#dffits_ctgry_df <- subset(dffits_df, prdl.descr.my.fctr %in% c("Unknown#0"))
#myplot_parcoord(obs_df=dffits_ctgry_df[, c(glb_id_var, glb_category_var, ".dffits", ".Bonf.p", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:5])], obs_ix=seq(1:nrow(dffits_ctgry_df))[!is.na(dffits_ctgry_df$.Bonf.p)], id_var=glb_id_var, category_var=glb_category_var)
#
#car::influenceIndexPlot(glb_models_lst[["RFE.X.glm"]]$finalModel, id.n=3)
myplot_parcoord <- function (obs_df, obs_ix=1:nrow(obs_df), id_var=".rownames", category_var=NULL) {
# Setup id_df & remove id_var from range computation
if (id_var != ".rownames") {
id_df <- obs_df[obs_ix, id_var, FALSE]
obs_df <- obs_df[, setdiff(names(obs_df), id_var), FALSE]
} else id_df <- data.frame(.rownames=row.names(obs_df)[obs_ix])
# Setup category_var -> Create a facet ???
category_df <- id_df
if (is.null(category_var)) {
category_var <- ".category"; category_df[, category_var] <- as.factor(0)
} else {
category_df[, category_var] <- obs_df[obs_ix, category_var]
obs_df <- obs_df[, setdiff(names(obs_df), category_var), FALSE]
}
ranges_mtrx <- apply(obs_df, 2L, range, na.rm = TRUE)
obs_scld_df <- as.data.frame(apply(obs_df, 2L,
function(feat) { feat_rng <- max(feat, na.rm = TRUE) - min(feat, na.rm = TRUE);
feat_rng <- ifelse(feat_rng == 0, 1, feat_rng);
return((feat - min(feat, na.rm = TRUE)) / feat_rng) }))
obsT_df <- as.data.frame(t(obs_df))
names(obsT_df) <- paste(".obs", names(obsT_df), sep=".");
obsT_df$.var.name <- row.names(obsT_df)
obsT_df$.var.pos <- 1:length(row.names(obsT_df))
obsST_df <- as.data.frame(t(obs_scld_df))
names(obsST_df) <- paste(".obs", names(obsST_df), sep=".");
obsST_df$.var.name <- row.names(obsST_df)
obsST_df$.var.pos <- 1:length(row.names(obsST_df))
plt_violin_df <- tidyr::gather(obsST_df, key=obs, value=value, -.var.name, -.var.pos)
obsHST_df <- as.data.frame(t(obs_scld_df[obs_ix, ]));
names(obsHST_df) <- as.character(id_df[, id_var])
obsHST_df$.var.name <- row.names(obsHST_df)
obsHST_df$.var.pos <- 1:length(row.names(obsHST_df))
#plt_df <- tidyr::gather(xt_df, key=obs, value=value, -c(.var.name, .var.pos))
# plt_df <- tidyr::gather_(xt_df, key=interp(id_var), value="value", quote(-c(.var.name, .var.pos)))
plt_obsHST_df <- tidyr::gather_(obsHST_df, key=interp(id_var), value="value",
-grep("(\\.var\\.name|\\.var\\.pos)", names(obsHST_df)))
ranges_df <- cbind(as.data.frame(ranges_mtrx), data.frame(.type=c("min", "max")))
ranges_df <- tidyr::gather(ranges_df, key=.var, value=value, -.type)
ranges_df$.y <- ifelse(ranges_df$.type == "min", -0.05, 1.05)
ranges_df <- merge(ranges_df, obsT_df[, c(".var.name", ".var.pos")],
by.x=".var", by.y=".var.name", all.x=TRUE)
ranges_df$.x <- ranges_df$.var.pos
ranges_df <- subset(ranges_df, select=-.var.pos)
plt_obsHST_df <- merge(plt_obsHST_df, category_df, x.all=TRUE)
# plt_obsHST_df[, category_var] <- NA
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11448, glb_category_var] <- "Unknown#0"
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11581, glb_category_var] <- "iPad4#1"
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11583, glb_category_var] <- "Unknown#0"
gp <- ggplot(plt_obsHST_df, aes(x=reorder(.var.name, .var.pos), y=value)) +
geom_violin(data=plt_violin_df, aes(x=reorder(.var.name, .var.pos), y=value),
color="grey80", scale="width") +
geom_line(data=plt_obsHST_df,
aes_string(group=id_var, color=id_var, linetype=category_var), size=1) +
geom_point(data=plt_obsHST_df, aes_string(shape=category_var), size=3) +
scale_color_brewer(type="qual", palette="Set1") +
geom_vline(xintercept=1:length(names(obs_df)), color="grey50") +
geom_text(data=ranges_df,
aes_string(x=".x", y=".y", label="myformat_number(value)"),
size=3.5) +
theme(axis.text.x=element_text(hjust=1, angle=45),
axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
xlab("") + ylab("")
# ggtitle("Dummy")
return(gp)
}
# myplot_parcoord(obs_df=glb_fitobs_df[, c(glb_id_var, glb_rsp_var,
# "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:5])], obs_ix=hatobs_ix, id_var=glb_id_var)
# myplot_parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], obs_ix=hatobs_ix)
# hatvals <- hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)
# hatobs_ix <- which(hatvals == max(hatvals))
# MASS::parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], var.label=TRUE)
#plot(hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel), type = "h")
#glb_fitobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
#all.equal(glb_models_lst[[glb_sel_mdl_id]], glb_models_lst[[glb_fin_mdl_id]])
glb_obstrn_outliers <- c(glb_obsfit_outliers
)
#car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel)
#glb_trnobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
glb_rfe_fit_sizes <- ### bid0_sp
c(106, 111, 116, 120, 128) # or NULL c(8, 16, 32, 64, 128, 140)
### bid1_sp
# c(8, 11, 16, 21, 32, 64, 128)
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glb_mdl_methods <- c(NULL
# deterministic
#, "lm",
, "glm"
#, "bayesglm" # crashing w/ parallel processing
, "glmnet", "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
#, "nnet" , "avNNet" # predicts 1 for all obs in bid0_sp # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
#, "svmPoly" # crashing w/ parallel processing #, "svmPoly" runs 75 models per cv sample for tunelength=5
#, "svmRadial" # crashing w/ parallel processing
, "earth", "bagEarth" # Takes a long time
#, "parRF" # crashing w/ parallel processing
)
} else
# Classification - Add ada,bagEarth (auto feature selection)
if (glb_is_binomial)
glb_mdl_methods <- c("glm", "bayesglm", "glmnet", "rpart", "rf", "gbm") else
glb_mdl_methods <- c("rpart", "rf", "gbm")
glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glb_mdl_methods)
glb_mdl_family_lst[["RFE.X"]] <- glb_mdl_methods
### bid1_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# # from RFE.X
# , "startprice.dgt1.is9", "startprice.dcm2.is9", "startprice.dcm1.is9", "startprice.dgt2.is9"
# #, "condition.fctr"
# , "prdl.descr.my.fctr", "color.fctr"
# #, "D.ratio.weight.sum.wrds.n"
# , "cellular.fctr", "cellular.fctr:carrier.fctr"
#
# # from RFE.X.Interact
# , "cellular.fctr:prdl.descr.my.fctr", "cellular.fctr:startprice.dgt2.is9", "cellular.fctr:startprice.dgt1.is9", "cellular.fctr:color.fctr"
# , "cellular.fctr:condition.fctr" # RMSE up with keeping condition.fctr in the model
# # RMSE & R.sq up with removing condition.fctr from the model
# , "cellular.fctr:D.ratio.weight.sum.wrds.n"
# )
### bid1_sp
glb_mdl_family_lst[["All.X"]] <- "glmnet"
glb_mdl_family_lst[["Best.Interact"]] <- "glmnet"
# Check if interaction features make fit better
# Check if tuning parameters make fit better
glb_tune_models_df <- data.frame()
#RFE.X.avNNet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
### bid1_sp
# size=[1] 3 5 7 9; decay=0 0.0001 [0.001] 0.01 0.1; bag=[FALSE]; RMSE=0.9285472
### bid0&1_sp
#RFE.X.bagEarth
### bid0_sp
#RFE.X.bagEarth degree=[1]; nprune=[33]; RMSE=0.1507259
### bid1_sp
#RFE.X.bagEarth degree=[1]; nprune=[32]; RMSE=0.6379639
#RFE.X.bagEarth degree=[1] 2 3; nprune=8 16 32 64 [128]; RMSE=0.6334405
#RFE.X.bagEarth degree=1 [2]; nprune=16 32 64 128 [256]; RMSE=0.6211924
#RFE.X.bagEarth degree=1 [2]; nprune=64 128 200 225 [256]; RMSE=0.6320776 (up)
#RFE.X.bagEarth degree=[1] 2; nprune=64 128 225 256 [275]; RMSE=0.640644 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 200 [256] 300; RMSE=0.6496039 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=32 64 128 256 [512]; RMSE=0.6404529 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
### bid0&1_sp
### bid0_sp
#RFE.X.earth degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
### bid0_sp
#RFE.X.gbm
### bid0_sp
# shrinkage=[0.1]; n.trees=50 100 150 [200] 250; RMSE=0.2062651
# shrinkage=0.00 0.05 0.10 0.15 [0.20]; n.trees=50 [100] 150 200 250; interaction.depth=1 [2] 3 4 5; n.minobsinnode=[10]; RMSE=0.2019453
# shrinkage=0.00 0.05 [0.10] 0.15 0.20; n.trees=50 100 150 200 [250]; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method="gbm", parameter="shrinkage", min=0.05, max=0.25, by=0.05)
,data.frame(method="gbm", parameter="n.trees", min=100, max=300, by=50)
,data.frame(method="gbm", parameter="interaction.depth", min=1, max=5, by=1)
,data.frame(method="gbm", parameter="n.minobsinnode", min=10, max=10, by=10)
#seq(from=0.05, to=0.25, by=0.05)
))
### bid1_sp
# shrinkage=[0.1]; n.trees=50 100 150 200 [250]; interaction.depth=1 2 3 4 [5]; n.minobsinnode=[10]; RMSE=0.5054172
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 [150] 200 250 300; interaction.depth=2 3 4 5 [6]; n.minobsinnode=6 [8] 10 12 14; RMSE=0.5036430
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 150 [200] 250 300; interaction.depth=3 4 5 [6] 7; n.minobsinnode=6 8 [10] 12 14; RMSE=0.502774
# shrinkage=0.04; n.trees=200; interaction.depth=6; n.minobsinnode=10; RMSE=0.502774
# shrinkage=[0.05] 0.10 0.15 0.20 0.25; n.trees=100 [150] 200 250 300; interaction.depth=2 3 [4] 5 6; n.minobsinnode=[10]; RMSE=0.5058678 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", vals = "0.04")
# ,data.frame(method = "gbm", parameter = "n.trees", vals = "200")
# ,data.frame(method = "gbm", parameter = "interaction.depth", vals = "6")
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", vals = "10")
# ))
### bid0&1_sp
#RFE.X.glmnet
### bid1_sp
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
### bid1_sp
#RFE.X.nnet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; RMSE=1.3300906
### bid1_sp
# size=1 3 5 7 [9]; decay=0e+00 1e-04 1e-03 1e-02 [1e-01]; RMSE=0.9289109
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
### bid0&1_sp
#RFE.X.rf
### bid0_sp
# mtry=2 35 [68] 101 134; RMSE=0.1331992
# mtry=2 35 68 [101] 134; RMSE=0.1339974
### bid0_sp
#RFE.X.rpart
### bid0_sp
# cp=[0.03230142] 0.06012801 0.09395662 0.12251081 0.35258370; RMSE=0.1771138
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method="rpart", parameter="cp", min=0.02, max=0.04, by=0.005)
))
### bid1_sp
# cp=[0.008081388] 0.016191995 0.027590245 0.299848193 0.361621486; RMSE=0.5294398
# cp=[0.005] 0.006 0.007 0.008 0.009 0.010; RMSE=0.522678
# cp=0.001 [0.003] 0.005 0.007 0.009; RMSE=0.5186586
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="rpart", parameter="cp", min=0.001, max=0.010, by=0.002)
# ))
### bid0&1_sp
#RFE.X.svmLinear
### bid0_sp
# C=[1]; RMSE=0.1374094
# C=1e-02 [0.1] 5e-01 1e+00 2e+00 3e+00 4e+00 1e+01 1e+02; RMSE=0.1271318
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
### bid1_sp
# C=[1]; RMSE=0.6614060
# C=1e-02 [1e-01] 1e+00 1e+01 1e+02; RMSE=0.6373977
# C=[0.05] 0.10 0.50 1.00 10.00; RMSE=0.6324697
# C=0.01 [0.05] 0.10 0.50 1.00; RMSE=0.6324697
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
))
### bid0&1_sp
#RFE.X.svmLinear2
### bid0_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.1276354
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
### bid1_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.6483622
# cost=[0.0625] 0.1250 0.25 0.50 1.00; RMSE=0.6335311
# cost=0.0312 [0.0625] 0.1250 0.25 0.50; RMSE=0.6335311
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0312 0.0625 0.125 0.25 0.50")
))
### bid0&1_sp
#RFE.X.svmPoly
### bid0_sp
# degree=[1] 2 3; scale=0.001 0.01 [0.1] 1 10; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1276130
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
))
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
))
### bid0_sp
#RFE.X.svmRadial
### bid0_sp
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
### bid0_sp
#data.frame(parameter="mtry", min=080, max=100, by=10),
#glb_to_sav(); all.equal(sav_models_df, glb_models_df)
#glb_models_df <- subset(sav_models_df, id != "RFE.X.gbm"); print(sort(glb_models_df$id))
glb_preproc_methods <- #NULL
### bid0_sp
c("YeoJohnson", "center.scale",
# crashes with train: all the RMSE metric values are missing
# probably due to interaction vars
"range", "pca", "ica",
"spatialSign")
### bid0_sp
### bid1_sp
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
### bid1_sp
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
#glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
glb_model_evl_criteria <- c("min.RMSE.fit", "max.Adj.R.sq.fit", "max.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.auc.OOB", "max.Accuracy.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <-
### bid0_sp
c("RFE.X.glm"
#, "RFE.X.bayesglm"
, "RFE.X.glmnet", "RFE.X.rpart", "RFE.X.gbm", "RFE.X.rf", "RFE.X.svmLinear", "RFE.X.svmLinear2"
#, "RFE.X.svmPoly", "RFE.X.svmRadial"
, "RFE.X.earth", "RFE.X.bagEarth", "RFE.X.Interact.glmnet", "RFE.X.YeoJohnson.glmnet", "RFE.X.center.scale.glmnet", "RFE.X.spatialSign.glmnet")
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
# c("RFE.X.spatialSign.rf", "RFE.X.YeoJohnson.rf", "RFE.X.center.scale.rf", "RFE.X.rf", "RFE.X.avNNet", "RFE.X.bagEarth", "RFE.X.earth", "RFE.X.gbm", "RFE.X.glmnet", "RFE.X.nnet", "RFE.X.svmLinear2", "RFE.X.glm", "RFE.X.svmLinear", "RFE.X.rpart")
### bid1_sp
glb_sel_mdl_id <- NULL #select from c(NULL, "RFE.X.glmnet")
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage")
glb_out_obs <- "all" # select from c(NULL, "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
#glb_out_vars_lst[[glb_id_var]] <- glb_id_var
glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
glb_out_vars_lst[[paste0(head(unlist(strsplit(glb_rsp_var_out, "")), -1), collapse = "")]] <-
"%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id)"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.311 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_to_sav <- function() {
sav_allobs_df <<- glb_allobs_df
sav_trnobs_df <<- glb_trnobs_df
if (any(grepl("glb_fitobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_fitobs_df)) sav_fitobs_df <<- glb_fitobs_df
if (any(grepl("glb_OOBobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_OOBobs_df)) sav_OOBobs_df <<- glb_OOBobs_df
if (any(grepl("glb_newobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_newobs_df)) {
#print("Attempting to save glb_newobs_df...")
sav_newobs_df <<- glb_newobs_df
}
if (any(grepl("glb_ctgry_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_ctgry_df)) sav_ctgry_df <<- glb_ctgry_df
if (!is.null(glb_models_lst )) sav_models_lst <<- glb_models_lst
if (!is.null(glb_models_df )) sav_models_df <<- glb_models_df
if (any(grepl("glb_feats_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_feats_df)) sav_feats_df <<- glb_feats_df
if (any(grepl("glb_featsimp_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_featsimp_df)) sav_featsimp_df <<- glb_featsimp_df
}
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here"); glb_to_sav()
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# if (!is.null(glb_drop_obs_condition))
# glb_allobs_df <- do.call("subset",
# list(glb_allobs_df, parse(text=paste0("!(", glb_drop_obs_condition, ")"))))
# - Merge glb_obs_stack_condition & glb_obs_drop_condition
# - Derive glb_obs_stack|drop_chk_vars from condition automatically
# - Implement glb_obs_stack_condition & glb_obs_stack_chk_vars options
dsp_partition_stats <- function(obs_df, vars=NULL) {
lcl_vars <- NULL
for (var in c(vars, glb_rsp_var_raw)) {
if ((length(unique(obs_df[, var])) > 5) && is.numeric(obs_df[, var])) {
cut_var <- paste0(var, ".cut.fctr")
obs_df[, cut_var] <- cut(obs_df[, var], 3)
lcl_vars <- union(lcl_vars, cut_var)
} else lcl_vars <- union(lcl_vars, var)
}
print("Partition stats:")
print(mycreate_sqlxtab_df(obs_df, union(lcl_vars, ".src")))
for (var in lcl_vars) {
print(freq_df <- mycreate_sqlxtab_df(obs_df, union(var, ".src")))
print(myplot_hbar(freq_df, ".src", ".n", colorcol_name=var))
}
print(mycreate_sqlxtab_df(obs_df, ".src"))
# if (length(unique(glb_allobs_df[, glb_rsp_var_raw])) > 5) {
# cut_var <- paste0(glb_rsp_var_raw, ".cut.fctr")
# glb_allobs_df[, cut_var] <- cut(glb_allobs_df[, glb_rsp_var_raw], 3)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, cut_var)
# glb_obs_stack_chk_vars <- union(cut_var, glb_obs_stack_chk_vars)
# } else glb_obs_stack_chk_vars <- union(glb_rsp_var_raw, glb_obs_stack_chk_vars)
# #glb_obs_stack_chk_vars <- union(glb_obs_stack_chk_vars, ".src")
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# print(mycreate_sqlxtab_df(glb_allobs_df, union(glb_obs_stack_chk_vars, ".src")))
# for (var in glb_obs_stack_chk_vars) {
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# }
# print(mycreate_sqlxtab_df(glb_allobs_df, ".src"))
}
myget_symbols <- function(txt) {
#print(getParseData(parse(text=txt, keep.source=TRUE)))
return(unique(subset(getParseData(parse(text=txt, keep.source=TRUE)),
token == "SYMBOL")$text))
}
# tokens <- unlist(strsplit(gsub("[[:punct:]|[:space:]]", " ", glb_obs_drop_condition), " "))
# tokens <- tokens[tokens != ""]
# glb_obs_drop_chk_vars <- c("biddable") # or NULL
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glb_obs_drop_condition))## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## UniqueID.cut.fctr productline biddable startprice.cut.fctr
## 1 (1e+04,1.09e+04] iPad 2 1 (-0.99,333]
## 2 (1e+04,1.09e+04] iPad 1 1 (-0.99,333]
## 3 (1.18e+04,1.27e+04] iPad 2 0 (-0.99,333]
## 4 (1.09e+04,1.18e+04] iPad 2 0 (-0.99,333]
## 5 (1e+04,1.09e+04] iPad mini 1 (-0.99,333]
## 6 (1.09e+04,1.18e+04] iPad mini 0 (-0.99,333]
## 7 (1.18e+04,1.27e+04] iPad 2 1 (-0.99,333]
## 8 (1.09e+04,1.18e+04] Unknown 0 (-0.99,333]
## 9 (1.18e+04,1.27e+04] iPad mini 1 (-0.99,333]
## 10 (1e+04,1.09e+04] iPad 3 1 (-0.99,333]
## 11 (1.09e+04,1.18e+04] iPad mini 1 (-0.99,333]
## 12 (1.09e+04,1.18e+04] iPad 1 0 (-0.99,333]
## 13 (1e+04,1.09e+04] iPad 2 0 (-0.99,333]
## 14 (1.09e+04,1.18e+04] iPad Air 2 0 (333,667]
## 15 (1.18e+04,1.27e+04] iPad 1 1 (-0.99,333]
## 16 (1.18e+04,1.27e+04] iPad mini 0 (-0.99,333]
## 17 (1.09e+04,1.18e+04] Unknown 1 (-0.99,333]
## 18 (1e+04,1.09e+04] iPad Air 1 (-0.99,333]
## 19 (1.18e+04,1.27e+04] Unknown 0 (-0.99,333]
## 20 (1e+04,1.09e+04] iPad 1 0 (-0.99,333]
## 21 (1e+04,1.09e+04] iPad mini 0 (-0.99,333]
## 22 (1e+04,1.09e+04] iPad 4 1 (-0.99,333]
## 23 (1.09e+04,1.18e+04] iPad 2 1 (-0.99,333]
## 24 (1.09e+04,1.18e+04] iPad Air 0 (333,667]
## 25 (1.18e+04,1.27e+04] iPad 1 0 (-0.99,333]
## 26 (1e+04,1.09e+04] iPad 4 0 (-0.99,333]
## 27 (1e+04,1.09e+04] iPad 3 0 (-0.99,333]
## 28 (1.18e+04,1.27e+04] iPad Air 1 (-0.99,333]
## 29 (1.18e+04,1.27e+04] Unknown 1 (-0.99,333]
## 30 (1.09e+04,1.18e+04] iPad mini 3 0 (333,667]
## 31 (1.18e+04,1.27e+04] iPad mini 2 1 (-0.99,333]
## 32 (1.09e+04,1.18e+04] iPad 4 0 (-0.99,333]
## 33 (1.18e+04,1.27e+04] iPad 3 0 (-0.99,333]
## 34 (1.18e+04,1.27e+04] iPad 4 0 (-0.99,333]
## 35 (1e+04,1.09e+04] Unknown 1 (-0.99,333]
## 36 (1.09e+04,1.18e+04] iPad 3 0 (-0.99,333]
## 37 (1e+04,1.09e+04] iPad Air 2 0 (333,667]
## 38 (1.18e+04,1.27e+04] iPad 4 1 (-0.99,333]
## 39 (1.18e+04,1.27e+04] iPad Air 2 0 (333,667]
## 40 (1e+04,1.09e+04] Unknown 0 (-0.99,333]
## 41 (1e+04,1.09e+04] iPad mini 2 1 (-0.99,333]
## 42 (1.09e+04,1.18e+04] iPad Air 1 (-0.99,333]
## 43 (1e+04,1.09e+04] iPad Air 0 (333,667]
## 44 (1.18e+04,1.27e+04] iPad 3 1 (-0.99,333]
## 45 (1.18e+04,1.27e+04] iPad Air 0 (333,667]
## 46 (1.09e+04,1.18e+04] iPad 1 1 (-0.99,333]
## 47 (1.09e+04,1.18e+04] iPad mini 2 0 (-0.99,333]
## 48 (1.09e+04,1.18e+04] Unknown 0 (333,667]
## 49 (1.18e+04,1.27e+04] iPad mini 3 0 (333,667]
## 50 (1e+04,1.09e+04] iPad Air 2 1 (-0.99,333]
## 51 (1.09e+04,1.18e+04] iPad 4 1 (-0.99,333]
## 52 (1.09e+04,1.18e+04] iPad mini 2 0 (333,667]
## 53 (1.09e+04,1.18e+04] iPad 3 1 (-0.99,333]
## 54 (1.09e+04,1.18e+04] iPad Air 2 1 (333,667]
## 55 (1e+04,1.09e+04] iPad Air 0 (-0.99,333]
## 56 (1.09e+04,1.18e+04] iPad Air 0 (-0.99,333]
## 57 (1.18e+04,1.27e+04] iPad mini 2 0 (-0.99,333]
## 58 (1.09e+04,1.18e+04] iPad mini 2 1 (-0.99,333]
## 59 (1.09e+04,1.18e+04] iPad Air 2 1 (-0.99,333]
## 60 (1.09e+04,1.18e+04] iPad mini 0 (333,667]
## 61 (1.18e+04,1.27e+04] iPad Air 0 (-0.99,333]
## 62 (1.09e+04,1.18e+04] iPad 4 0 (333,667]
## 63 (1.09e+04,1.18e+04] iPad Air 2 0 (667,1e+03]
## 64 (1.18e+04,1.27e+04] iPad Air 2 1 (-0.99,333]
## 65 (1e+04,1.09e+04] iPad Air 2 1 (333,667]
## 66 (1e+04,1.09e+04] iPad mini 2 0 (-0.99,333]
## 67 (1.18e+04,1.27e+04] iPad 4 0 (333,667]
## 68 (1e+04,1.09e+04] iPad mini 3 0 (333,667]
## 69 (1e+04,1.09e+04] iPad mini 3 1 (-0.99,333]
## 70 (1.18e+04,1.27e+04] Unknown 0 (-0.99,333]
## 71 (1.18e+04,1.27e+04] iPad 1 0 (-0.99,333]
## 72 (1.18e+04,1.27e+04] iPad Air 2 1 (333,667]
## 73 (1.18e+04,1.27e+04] Unknown 0 (333,667]
## 74 (1.18e+04,1.27e+04] iPad mini 3 1 (-0.99,333]
## 75 (1.18e+04,1.27e+04] iPad Air 2 0 (667,1e+03]
## 76 (1.18e+04,1.27e+04] iPad 4 0 (-0.99,333]
## 77 (1.18e+04,1.27e+04] iPad Air 2 0 (333,667]
## 78 (1.09e+04,1.18e+04] iPad 2 0 (333,667]
## 79 (1e+04,1.09e+04] iPad mini 2 0 (333,667]
## 80 (1.09e+04,1.18e+04] iPad 3 0 (333,667]
## 81 (1.09e+04,1.18e+04] iPad mini 3 0 (-0.99,333]
## 82 (1.09e+04,1.18e+04] iPad mini 3 1 (-0.99,333]
## 83 (1.09e+04,1.18e+04] iPad mini 3 1 (333,667]
## 84 (1.18e+04,1.27e+04] iPad 2 0 (-0.99,333]
## 85 (1.18e+04,1.27e+04] iPad mini 0 (-0.99,333]
## 86 (1.18e+04,1.27e+04] iPad mini 2 0 (333,667]
## 87 (1e+04,1.09e+04] iPad Air 1 (333,667]
## 88 (1e+04,1.09e+04] iPad mini 0 (333,667]
## 89 (1.18e+04,1.27e+04] Unknown 1 (333,667]
## 90 (1.18e+04,1.27e+04] iPad 3 0 (-0.99,333]
## 91 (1.18e+04,1.27e+04] iPad Air 0 (-0.99,333]
## 92 (1e+04,1.09e+04] iPad 4 0 (333,667]
## 93 (1e+04,1.09e+04] iPad mini 3 0 (-0.99,333]
## 94 (1.09e+04,1.18e+04] Unknown 1 (333,667]
## 95 (1.09e+04,1.18e+04] iPad Air 0 (667,1e+03]
## 96 (1.18e+04,1.27e+04] iPad mini 0 (333,667]
## 97 (1.18e+04,1.27e+04] iPad mini 3 0 (-0.99,333]
## 98 (1.09e+04,1.18e+04] iPad Air 2 0 (-0.99,333]
## 99 (1.09e+04,1.18e+04] iPad mini 2 1 (333,667]
## 100 (1.18e+04,1.27e+04] iPad 1 1 (-0.99,333]
## 101 (1.18e+04,1.27e+04] iPad 2 0 (333,667]
## 102 (1.18e+04,1.27e+04] iPad mini 1 (-0.99,333]
## 103 (1.18e+04,1.27e+04] iPad mini 2 0 (-0.99,333]
## 104 (1.18e+04,1.27e+04] iPad mini 2 1 (333,667]
## 105 (1.18e+04,1.27e+04] iPad mini 3 0 (333,667]
## 106 (1.18e+04,1.27e+04] iPad mini 3 1 (333,667]
## 107 (1e+04,1.09e+04] iPad mini 3 1 (333,667]
## 108 (1.09e+04,1.18e+04] iPad 4 1 (333,667]
## 109 (1.09e+04,1.18e+04] iPad mini 0 (667,1e+03]
## 110 (1.09e+04,1.18e+04] iPad mini 1 (333,667]
## 111 (1.09e+04,1.18e+04] iPad mini 3 0 (667,1e+03]
## 112 (1.09e+04,1.18e+04] iPad mini 3 1 (667,1e+03]
## 113 (1.09e+04,1.18e+04] iPad mini Retina 0 (-0.99,333]
## 114 (1.09e+04,1.18e+04] iPad mini Retina 0 (333,667]
## 115 (1.18e+04,1.27e+04] Unknown 0 (333,667]
## 116 (1.18e+04,1.27e+04] Unknown 0 (667,1e+03]
## 117 (1.18e+04,1.27e+04] Unknown 1 (-0.99,333]
## 118 (1.18e+04,1.27e+04] iPad 2 1 (-0.99,333]
## 119 (1.18e+04,1.27e+04] iPad 3 0 (333,667]
## 120 (1.18e+04,1.27e+04] iPad 4 1 (-0.99,333]
## 121 (1.18e+04,1.27e+04] iPad Air 0 (333,667]
## 122 (1.18e+04,1.27e+04] iPad Air 0 (667,1e+03]
## 123 (1.18e+04,1.27e+04] iPad Air 1 (333,667]
## 124 (1.18e+04,1.27e+04] iPad Air 2 1 (667,1e+03]
## 125 (1e+04,1.09e+04] Unknown 0 (333,667]
## 126 (1e+04,1.09e+04] iPad 4 1 (333,667]
## 127 (1e+04,1.09e+04] iPad Air 2 0 (-0.99,333]
## 128 (1e+04,1.09e+04] iPad mini 2 1 (333,667]
## 129 (1e+04,1.09e+04] iPad mini Retina 0 (-0.99,333]
## 130 (1.09e+04,1.18e+04] Unknown 0 (667,1e+03]
## 131 (1.09e+04,1.18e+04] Unknown 1 (667,1e+03]
## 132 (1.09e+04,1.18e+04] iPad 1 1 (333,667]
## 133 (1.09e+04,1.18e+04] iPad 4 0 (667,1e+03]
## 134 (1.09e+04,1.18e+04] iPad 5 1 (-0.99,333]
## 135 (1.09e+04,1.18e+04] iPad Air 1 (333,667]
## 136 (1.09e+04,1.18e+04] iPad Air 2 1 (667,1e+03]
## 137 (1.18e+04,1.27e+04] iPad 2 0 (667,1e+03]
## 138 (1.18e+04,1.27e+04] iPad 3 1 (-0.99,333]
## 139 (1.18e+04,1.27e+04] iPad Air 2 0 (-0.99,333]
## 140 (1.18e+04,1.27e+04] iPad Air 2 1 (333,667]
## 141 (1.18e+04,1.27e+04] iPad mini 0 (333,667]
## 142 (1.18e+04,1.27e+04] iPad mini 3 0 (-0.99,333]
## 143 (1.18e+04,1.27e+04] iPad mini 3 0 (667,1e+03]
## 144 (1.18e+04,1.27e+04] iPad mini 3 1 (-0.99,333]
## 145 (1.18e+04,1.27e+04] iPad mini 3 1 (667,1e+03]
## 146 (1.18e+04,1.27e+04] iPad mini Retina 0 (333,667]
## 147 (1e+04,1.09e+04] iPad 2 0 (333,667]
## 148 (1e+04,1.09e+04] iPad 3 0 (333,667]
## 149 (1e+04,1.09e+04] iPad 3 0 (667,1e+03]
## 150 (1e+04,1.09e+04] iPad 3 1 (333,667]
## 151 (1e+04,1.09e+04] iPad Air 2 0 (667,1e+03]
## 152 (1e+04,1.09e+04] iPad Air 2 1 (667,1e+03]
## 153 (1e+04,1.09e+04] iPad mini Retina 1 (-0.99,333]
## .src .n
## 1 Train 100
## 2 Train 93
## 3 Test 80
## 4 Train 77
## 5 Train 74
## 6 Train 70
## 7 Test 70
## 8 Train 59
## 9 Test 59
## 10 Train 58
## 11 Train 57
## 12 Train 56
## 13 Train 55
## 14 Train 50
## 15 Test 50
## 16 Test 48
## 17 Train 47
## 18 Train 43
## 19 Test 42
## 20 Train 42
## 21 Train 42
## 22 Train 40
## 23 Train 38
## 24 Train 38
## 25 Test 38
## 26 Train 36
## 27 Train 35
## 28 Test 34
## 29 Test 33
## 30 Train 31
## 31 Test 31
## 32 Train 30
## 33 Test 30
## 34 Test 30
## 35 Train 30
## 36 Train 28
## 37 Train 28
## 38 Test 26
## 39 Test 26
## 40 Train 26
## 41 Train 26
## 42 Train 24
## 43 Train 24
## 44 Test 23
## 45 Test 22
## 46 Train 21
## 47 Train 20
## 48 Train 19
## 49 Test 19
## 50 Train 19
## 51 Train 18
## 52 Train 18
## 53 Train 17
## 54 Train 17
## 55 Train 17
## 56 Train 16
## 57 Test 16
## 58 Train 15
## 59 Train 14
## 60 Train 14
## 61 Test 14
## 62 Train 13
## 63 Train 13
## 64 Test 13
## 65 Train 13
## 66 Train 13
## 67 Test 12
## 68 Train 12
## 69 Train 12
## 70 Train 11
## 71 Train 11
## 72 Test 11
## 73 Test 10
## 74 Test 10
## 75 Test 9
## 76 Train 8
## 77 Train 8
## 78 Train 7
## 79 Train 7
## 80 Train 6
## 81 Train 6
## 82 Train 6
## 83 Train 6
## 84 Train 6
## 85 Train 6
## 86 Test 6
## 87 Train 6
## 88 Train 6
## 89 Test 5
## 90 Train 5
## 91 Train 5
## 92 Train 5
## 93 Train 5
## 94 Train 4
## 95 Train 4
## 96 Test 4
## 97 Test 4
## 98 Train 3
## 99 Train 3
## 100 Train 3
## 101 Test 3
## 102 Train 3
## 103 Train 3
## 104 Test 3
## 105 Train 3
## 106 Test 3
## 107 Train 3
## 108 Train 2
## 109 Train 2
## 110 Train 2
## 111 Train 2
## 112 Train 2
## 113 Train 2
## 114 Train 2
## 115 Train 2
## 116 Test 2
## 117 Train 2
## 118 Train 2
## 119 Test 2
## 120 Train 2
## 121 Train 2
## 122 Test 2
## 123 Test 2
## 124 Test 2
## 125 Train 2
## 126 Train 2
## 127 Train 2
## 128 Train 2
## 129 Train 2
## 130 Train 1
## 131 Train 1
## 132 Train 1
## 133 Train 1
## 134 Train 1
## 135 Train 1
## 136 Train 1
## 137 Test 1
## 138 Train 1
## 139 Test 1
## 140 Train 1
## 141 Train 1
## 142 Train 1
## 143 Test 1
## 144 Train 1
## 145 Test 1
## 146 Train 1
## 147 Train 1
## 148 Train 1
## 149 Train 1
## 150 Train 1
## 151 Train 1
## 152 Train 1
## 153 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 887
## 2 (1.09e+04,1.18e+04] Train 886
## 3 (1.18e+04,1.27e+04] Test 798
## 4 (1.18e+04,1.27e+04] Train 88
## productline .src .n
## 1 iPad 2 Train 286
## 2 iPad mini Train 277
## 3 iPad 1 Train 227
## 4 Unknown Train 204
## 5 iPad Air Train 180
## 6 iPad Air 2 Train 171
## 7 iPad 4 Train 157
## 8 iPad 2 Test 154
## 9 iPad 3 Train 153
## 10 iPad mini Test 111
## 11 iPad mini 2 Train 107
## 12 Unknown Test 92
## 13 iPad mini 3 Train 90
## 14 iPad 1 Test 88
## 15 iPad Air Test 74
## 16 iPad 4 Test 68
## 17 iPad Air 2 Test 62
## 18 iPad mini 2 Test 56
## 19 iPad 3 Test 55
## 20 iPad mini 3 Test 38
## 21 iPad mini Retina Train 8
## 22 iPad 5 Train 1
## biddable .src .n
## 1 0 Train 1024
## 2 1 Train 837
## 3 0 Test 422
## 4 1 Test 376
## startprice.cut.fctr .src .n
## 1 (-0.99,333] Train 1466
## 2 (-0.99,333] Test 652
## 3 (333,667] Train 365
## 4 (333,667] Test 128
## 5 (667,1e+03] Train 30
## 6 (667,1e+03] Test 18
## .src .n
## 1 Train 1861
## 2 Test 798if (!is.null(glb_obs_drop_condition)) {
print(sprintf("Running glb_obs_drop_condition filter: %s", glb_obs_drop_condition))
glb_allobs_df <- do.call("subset",
list(glb_allobs_df, parse(text=paste0("!(", glb_obs_drop_condition, ")"))))
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glb_obs_drop_condition))
}## [1] "Running glb_obs_drop_condition filter: (UniqueID %in% c(NULL\n , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1\n , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1\n )) | (productline %in% c('iPad 5', 'iPad mini Retina')) |\n (biddable != 0)"
## [1] "Partition stats:"
## UniqueID.cut.fctr productline biddable startprice.cut.fctr .src .n
## 1 (1.18e+04,1.27e+04] iPad 2 0 (1.99,335] Test 80
## 2 (1.09e+04,1.18e+04] iPad 2 0 (1.99,335] Train 77
## 3 (1.09e+04,1.18e+04] iPad mini 0 (1.99,335] Train 70
## 4 (1.09e+04,1.18e+04] Unknown 0 (1.99,335] Train 59
## 5 (1.09e+04,1.18e+04] iPad 1 0 (1.99,335] Train 55
## 6 (1e+04,1.09e+04] iPad 2 0 (1.99,335] Train 55
## 7 (1.09e+04,1.18e+04] iPad Air 2 0 (335,668] Train 50
## 8 (1.18e+04,1.27e+04] iPad mini 0 (1.99,335] Test 49
## 9 (1.18e+04,1.27e+04] Unknown 0 (1.99,335] Test 42
## 10 (1e+04,1.09e+04] iPad 1 0 (1.99,335] Train 42
## 11 (1e+04,1.09e+04] iPad mini 0 (1.99,335] Train 42
## 12 (1.18e+04,1.27e+04] iPad 1 0 (1.99,335] Test 38
## 13 (1.09e+04,1.18e+04] iPad Air 0 (335,668] Train 37
## 14 (1e+04,1.09e+04] iPad 4 0 (1.99,335] Train 36
## 15 (1e+04,1.09e+04] iPad 3 0 (1.99,335] Train 35
## 16 (1.09e+04,1.18e+04] iPad mini 3 0 (335,668] Train 31
## 17 (1.09e+04,1.18e+04] iPad 4 0 (1.99,335] Train 30
## 18 (1.18e+04,1.27e+04] iPad 3 0 (1.99,335] Test 30
## 19 (1.18e+04,1.27e+04] iPad 4 0 (1.99,335] Test 30
## 20 (1.09e+04,1.18e+04] iPad 3 0 (1.99,335] Train 28
## 21 (1e+04,1.09e+04] iPad Air 2 0 (335,668] Train 28
## 22 (1e+04,1.09e+04] Unknown 0 (1.99,335] Train 27
## 23 (1.18e+04,1.27e+04] iPad Air 2 0 (335,668] Test 26
## 24 (1e+04,1.09e+04] iPad Air 0 (335,668] Train 24
## 25 (1.18e+04,1.27e+04] iPad Air 0 (335,668] Test 22
## 26 (1.09e+04,1.18e+04] iPad mini 2 0 (1.99,335] Train 20
## 27 (1.09e+04,1.18e+04] Unknown 0 (335,668] Train 19
## 28 (1.09e+04,1.18e+04] iPad mini 2 0 (335,668] Train 18
## 29 (1.18e+04,1.27e+04] iPad mini 3 0 (335,668] Test 18
## 30 (1.09e+04,1.18e+04] iPad Air 0 (1.99,335] Train 17
## 31 (1e+04,1.09e+04] iPad Air 0 (1.99,335] Train 17
## 32 (1.18e+04,1.27e+04] iPad mini 2 0 (1.99,335] Test 16
## 33 (1.09e+04,1.18e+04] iPad mini 0 (335,668] Train 14
## 34 (1.18e+04,1.27e+04] iPad Air 0 (1.99,335] Test 14
## 35 (1.09e+04,1.18e+04] iPad 4 0 (335,668] Train 13
## 36 (1.09e+04,1.18e+04] iPad Air 2 0 (668,1e+03] Train 13
## 37 (1e+04,1.09e+04] iPad mini 2 0 (1.99,335] Train 13
## 38 (1.18e+04,1.27e+04] iPad 4 0 (335,668] Test 12
## 39 (1.18e+04,1.27e+04] Unknown 0 (1.99,335] Train 11
## 40 (1e+04,1.09e+04] iPad mini 3 0 (335,668] Train 11
## 41 (1.18e+04,1.27e+04] Unknown 0 (335,668] Test 10
## 42 (1.18e+04,1.27e+04] iPad 1 0 (1.99,335] Train 10
## 43 (1.18e+04,1.27e+04] iPad Air 2 0 (668,1e+03] Test 9
## 44 (1.18e+04,1.27e+04] iPad 4 0 (1.99,335] Train 8
## 45 (1.18e+04,1.27e+04] iPad Air 2 0 (335,668] Train 8
## 46 (1.09e+04,1.18e+04] iPad 2 0 (335,668] Train 7
## 47 (1e+04,1.09e+04] iPad mini 2 0 (335,668] Train 7
## 48 (1.09e+04,1.18e+04] iPad 3 0 (335,668] Train 6
## 49 (1.09e+04,1.18e+04] iPad mini 3 0 (1.99,335] Train 6
## 50 (1.18e+04,1.27e+04] iPad 2 0 (1.99,335] Train 6
## 51 (1.18e+04,1.27e+04] iPad mini 0 (1.99,335] Train 6
## 52 (1.18e+04,1.27e+04] iPad mini 2 0 (335,668] Test 6
## 53 (1e+04,1.09e+04] iPad mini 0 (335,668] Train 6
## 54 (1e+04,1.09e+04] iPad mini 3 0 (1.99,335] Train 6
## 55 (1.18e+04,1.27e+04] iPad 3 0 (1.99,335] Train 5
## 56 (1.18e+04,1.27e+04] iPad Air 0 (1.99,335] Train 5
## 57 (1.18e+04,1.27e+04] iPad mini 3 0 (1.99,335] Test 5
## 58 (1e+04,1.09e+04] iPad 4 0 (335,668] Train 5
## 59 (1.09e+04,1.18e+04] iPad Air 0 (668,1e+03] Train 4
## 60 (1.09e+04,1.18e+04] iPad Air 2 0 (1.99,335] Train 3
## 61 (1.18e+04,1.27e+04] iPad 2 0 (335,668] Test 3
## 62 (1.18e+04,1.27e+04] iPad mini 0 (335,668] Test 3
## 63 (1.18e+04,1.27e+04] iPad mini 2 0 (1.99,335] Train 3
## 64 (1.18e+04,1.27e+04] iPad mini 3 0 (335,668] Train 3
## 65 (1.09e+04,1.18e+04] iPad mini 0 (668,1e+03] Train 2
## 66 (1.09e+04,1.18e+04] iPad mini 3 0 (668,1e+03] Train 2
## 67 (1.18e+04,1.27e+04] Unknown 0 (335,668] Train 2
## 68 (1.18e+04,1.27e+04] Unknown 0 (668,1e+03] Test 2
## 69 (1.18e+04,1.27e+04] iPad 3 0 (335,668] Test 2
## 70 (1.18e+04,1.27e+04] iPad Air 0 (335,668] Train 2
## 71 (1.18e+04,1.27e+04] iPad Air 0 (668,1e+03] Test 2
## 72 (1e+04,1.09e+04] iPad Air 2 0 (1.99,335] Train 2
## 73 (1.09e+04,1.18e+04] Unknown 0 (668,1e+03] Train 1
## 74 (1.09e+04,1.18e+04] iPad 4 0 (668,1e+03] Train 1
## 75 (1.18e+04,1.27e+04] iPad 2 0 (668,1e+03] Test 1
## 76 (1.18e+04,1.27e+04] iPad Air 2 0 (1.99,335] Test 1
## 77 (1.18e+04,1.27e+04] iPad mini 0 (335,668] Train 1
## 78 (1.18e+04,1.27e+04] iPad mini 3 0 (1.99,335] Train 1
## 79 (1.18e+04,1.27e+04] iPad mini 3 0 (668,1e+03] Test 1
## 80 (1e+04,1.09e+04] Unknown 0 (335,668] Train 1
## 81 (1e+04,1.09e+04] iPad 2 0 (335,668] Train 1
## 82 (1e+04,1.09e+04] iPad 3 0 (335,668] Train 1
## 83 (1e+04,1.09e+04] iPad 3 0 (668,1e+03] Train 1
## 84 (1e+04,1.09e+04] iPad Air 2 0 (668,1e+03] Train 1
## UniqueID.cut.fctr .src .n
## 1 (1.09e+04,1.18e+04] Train 583
## 2 (1.18e+04,1.27e+04] Test 422
## 3 (1e+04,1.09e+04] Train 361
## 4 (1.18e+04,1.27e+04] Train 71
## productline .src .n
## 1 iPad 2 Train 146
## 2 iPad mini Train 141
## 3 Unknown Train 120
## 4 iPad 1 Train 107
## 5 iPad Air Train 106
## 6 iPad Air 2 Train 105
## 7 iPad 4 Train 93
## 8 iPad 2 Test 84
## 9 iPad 3 Train 76
## 10 iPad mini 2 Train 61
## 11 iPad mini 3 Train 60
## 12 Unknown Test 54
## 13 iPad mini Test 52
## 14 iPad 4 Test 42
## 15 iPad 1 Test 38
## 16 iPad Air Test 38
## 17 iPad Air 2 Test 36
## 18 iPad 3 Test 32
## 19 iPad mini 3 Test 24
## 20 iPad mini 2 Test 22
## biddable .src .n
## 1 0 Train 1015
## 2 0 Test 422
## startprice.cut.fctr .src .n
## 1 (1.99,335] Train 695
## 2 (1.99,335] Test 305
## 3 (335,668] Train 295
## 4 (335,668] Test 102
## 5 (668,1e+03] Train 25
## 6 (668,1e+03] Test 15
## .src .n
## 1 Train 1015
## 2 Test 422# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 139 duplicates by all features:"myprint_df(dup_allobs_df)## description
## 385
## 390
## 1204
## 1315
## 2464
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## biddable startprice condition cellular carrier color storage
## 385 0 15 Used 0 None Black 16
## 390 0 15 Used 0 None Black 16
## 1204 0 15 Used 0 None Black 16
## 1315 0 399 Used Unknown AT&T White 32
## 2464 0 399 Used Unknown AT&T White 32
## 1029 0 108 Used 1 AT&T White 32
## productline sold UniqueID .src
## 385 Unknown 0 10385 Train
## 390 Unknown 0 10390 Train
## 1204 Unknown 0 11204 Train
## 1315 Unknown 0 11315 Train
## 2464 Unknown NA 12464 Test
## 1029 Unknown 0 11029 Train
## description
## 934 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 737 Near perfect cosmetic, perfect working condition.
## 1605
## 239 Like New
## 1167
## 1569
## biddable startprice condition cellular carrier color
## 934 0 162.00 Used 1 Verizon White
## 737 0 220.00 Used 0 None Unknown
## 1605 0 229.00 For parts or not working Unknown Unknown Unknown
## 239 0 398.99 Used 0 None Unknown
## 1167 0 375.00 Used 0 None White
## 1569 0 375.00 Used 0 None White
## storage productline sold UniqueID .src
## 934 16 iPad 2 0 10934 Train
## 737 16 iPad 3 0 10737 Train
## 1605 16 iPad Air 1 11605 Train
## 239 128 iPad Air 0 10239 Train
## 1167 64 iPad mini 2 1 11167 Train
## 1569 64 iPad mini 2 0 11569 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 18 18 0 2 2 4
## 1 1 3 0 0 3
## 16 16 0 3 0 3
## 31 31 3 0 0 3
## 37 37 3 0 0 3
## 55 55 3 0 0 3
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 23 23 1 0 1 2
## 28 28 2 0 0 2
## 33 33 1 1 0 2
## 45 45 2 0 0 2
## 48 48 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 61 61 2 0 0 2
## 62 62 1 0 1 2
## 63 63 1 1 0 2
## 64 64 2 0 0 2
## 65 65 0 1 1 2
## 66 66 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 8 8 1 1 0 2
## 9 9 1 1 0 2
## 27 27 1 1 0 2
## 32 32 1 1 0 2
## 33 33 1 1 0 2
## 43 43 1 1 0 2
## 47 47 1 1 0 2
## 57 57 1 1 0 2
## 63 63 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
#mydsp_obs(list(productline.contains="iPad 1", storage.contains="16", color.contains="Black", carrier.contains="None", cellular.contains="0", condition.contains="Used", startprice=80), cols=c("productline", "storage", "color", "carrier", "cellular", "condition", "startprice", "sold"))
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 2 2 1 0 1 2
## 4 4 0 0 2 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 11 11 1 0 1 2
## 13 13 1 0 1 2
## 15 15 0 1 1 2
## 17 17 0 0 2 2
## 18 18 0 2 2 4
## 19 19 0 1 1 2
## 20 20 0 0 2 2
## 21 21 1 0 1 2
## 22 22 1 0 1 2
## 23 23 1 0 1 2
## 24 24 1 0 1 2
## 25 25 0 0 2 2
## 26 26 1 0 1 2
## 35 35 0 0 2 2
## 36 36 1 0 1 2
## 38 38 1 0 1 2
## 39 39 0 0 2 2
## 41 41 0 1 1 2
## 42 42 1 0 1 2
## 46 46 0 1 1 2
## 48 48 0 1 1 2
## 49 49 1 0 1 2
## 52 52 0 1 1 2
## 53 53 1 0 1 2
## 56 56 1 0 1 2
## 62 62 1 0 1 2
## 65 65 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
# !_sp
# spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
# if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between spd_allobs_df & glb_allobs_df")
# mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
# if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between mrg_allobs_df & glb_allobs_df")
# mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
# (exp(mrg_allobs_df$startprice.log.predict.) - 1)
# mrg_allobs_df$startprice.log.diff <- log(1 + mrg_allobs_df$startprice) -
# mrg_allobs_df$startprice.log.predict.
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
# colorcol_name = "biddable"))
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.log.diff",
# colorcol_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.diff",
# fill_col_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.log.diff",
# fill_col_name = "biddable"))
# glb_allobs_df <- mrg_allobs_df
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
# "startprice.diff", "startprice.log", "startprice.log.predict.")
###
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
dsp_partition_stats(obs_df=glb_allobs_df,
vars=myget_symbols(glb_obs_repartition_train_condition))## [1] "Partition stats:"
## sold startprice.cut.fctr .src .n
## 1 0 (1.99,335] Train 526
## 2 NA (1.99,335] Test 305
## 3 0 (335,668] Train 248
## 4 1 (1.99,335] Train 169
## 5 NA (335,668] Test 102
## 6 1 (335,668] Train 47
## 7 0 (668,1e+03] Train 24
## 8 NA (668,1e+03] Test 15
## 9 1 (668,1e+03] Train 1
## sold .src .n
## 1 0 Train 798
## 2 NA Test 422
## 3 1 Train 217
## startprice.cut.fctr .src .n
## 1 (1.99,335] Train 695
## 2 (1.99,335] Test 305
## 3 (335,668] Train 295
## 4 (335,668] Test 102
## 5 (668,1e+03] Train 25
## 6 (668,1e+03] Test 15
## .src .n
## 1 Train 1015
## 2 Test 422if (!is.null(glb_obs_repartition_train_condition)) {
print(sprintf("Running glb_obs_repartition_train_condition filter: %s",
glb_obs_repartition_train_condition))
# glb_allobs_df <- mutate(glb_allobs_df, .src=ifelse(!is.na(sold) & (sold == 1),
# "Train", "Test"))
# glb_allobs_df <- mutate_(glb_allobs_df,
# .src=interp(ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")))
# glb_allobs_df <- within(glb_allobs_df, {
# .src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df <- within(glb_allobs_df, {
# if(eval(parse(text="!is.na(sold) & (sold == 1)"))) .src <- "Train" else
# .src <- "Test"
# })
# with(glb_allobs_df, {
# src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df$.src <- sapply(1:nrow(glb_allobs_df), function (row_ix) ifelse)
# glb_allobs_df[parse(text=paste0("!(", glb_obs_drop_condition, ")")), ".src"] <- do.call("subset",
# list(glb_allobs_df, ))
glb_trnobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0(" (", glb_obs_repartition_train_condition, ")"))))
glb_trnobs_df$.src <- "Train"
glb_newobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0("!(", glb_obs_repartition_train_condition, ")"))))
glb_newobs_df$.src <- "Test"
glb_allobs_df <- rbind(glb_trnobs_df, glb_newobs_df)
dsp_partition_stats(obs_df=glb_allobs_df,
vars=myget_symbols(glb_obs_repartition_train_condition))
}## [1] "Running glb_obs_repartition_train_condition filter: !is.na(sold) & (sold == 1)"
## [1] "Partition stats:"
## sold startprice.cut.fctr .src .n
## 1 0 (1.99,335] Test 526
## 2 NA (1.99,335] Test 305
## 3 0 (335,668] Test 248
## 4 1 (1.99,335] Train 169
## 5 NA (335,668] Test 102
## 6 1 (335,668] Train 47
## 7 0 (668,1e+03] Test 24
## 8 NA (668,1e+03] Test 15
## 9 1 (668,1e+03] Train 1
## sold .src .n
## 1 0 Test 798
## 2 NA Test 422
## 3 1 Train 217
## startprice.cut.fctr .src .n
## 1 (1.99,335] Test 831
## 2 (335,668] Test 350
## 3 (1.99,335] Train 169
## 4 (335,668] Train 47
## 5 (668,1e+03] Test 39
## 6 (668,1e+03] Train 1
## .src .n
## 1 Test 1220
## 2 Train 217# Only for _sp
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
# glb_allobs_df$.src <- "Test"
# glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.311 25.603 15.292
## 2 inspect.data 2 0 0 25.603 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## [1] "numeric data missing in : "
## sold
## 422
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 798
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 760 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## startprice startprice.log10 .n
## 1 199.99 2.301008 28
## 2 250.00 2.397940 23
## 3 300.00 2.477121 23
## 4 150.00 2.176091 21
## 5 200.00 2.301030 20
## 6 175.00 2.243038 19
## startprice startprice.log10 .n
## 4 150.00 2.176091 21
## 110 125.00 2.096910 3
## 211 439.98 2.643433 2
## 216 509.00 2.706718 2
## 398 246.00 2.390935 1
## 504 469.00 2.671173 1
## startprice startprice.log10 .n
## 580 899.99 2.954238 1
## 581 900.00 2.954243 1
## 582 939.00 2.972666 1
## 583 948.98 2.977257 1
## 584 999.00 2.999565 1
## 585 999.99 2.999996 1
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
set.seed(169)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 25.603 39.633 14.03
## 3 scrub.data 2 1 1 39.633 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 422
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 798
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 760 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Sprint T-Mobile Unknown Verizon
## 0 0 798 0 0 0 0
## 1 172 0 25 17 98 120
## Unknown 3 3 0 0 201 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Sprint T-Mobile Unknown Verizon
## 0 0 801 0 0 0 0
## 1 175 0 25 17 98 120
## Unknown 0 0 0 0 201 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 39.633 45.046 5.413
## 4 transform.data 2 2 2 45.046 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: startprice.log10.cut.fctr..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10029, 10948, 10136, 10178, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
# glb_allobs_df[row_pos, "descr.my"] <-
# gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 45.046 45.715 0.669
## 5 extract.features 3 0 0 45.715 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor label_minor bgn end
## 1 extract.features_bgn 1 0 0 45.723 NA
## elapsed
## 1 NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor label_minor
## 1 extract.features_bgn 1 0 0
## 2 extract.features_factorize.str.vars 2 0 0
## bgn end elapsed
## 1 45.723 45.74 0.017
## 2 45.740 NA NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid descr.my
## "storage" "productline" ".src" ".grpid" "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 6## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
#print(txt_vctr <- glb_allobs_df[glb_allobs_df$UniqueID == 11329, "descr.my"])
#strsplit(txt_vctr, "")[[1]][1]
#ptn_ix <- 2; glb_txt_map_df[ptn_ix, ]
#gsub(glb_txt_map_df[ptn_ix, "rex_str"], glb_txt_map_df[ptn_ix, "rpl_str"], txt_vctr)
#print(match_lst <- gregexpr(glb_txt_map_df[ptn_ix, "rex_str"], txt_vctr))
#strsplit(glb_txt_map_df[ptn_ix, "rex_str"], "")[[1]]
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_chr_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_chr_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_chr_lst; all.equal(sav_txt_lst, glb_txt_chr_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_chr_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_chr_lst <- list();
print(sprintf("Building glb_txt_chr_lst..."))
glb_txt_chr_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
names(txt_vctr) <- glb_allobs_df[, glb_id_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_chr_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_chr_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_chr_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
# Enhancements:
# - arg should be txt_corpus instead of txt_vctr
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE, lazy=FALSE)
# Defaulting to Tf since TfIdf with normalize = TRUE throws a warning for empty docs
terms_mtrx <- as.matrix(TermDocumentMatrix(txt_corpus, control=list(weighting=weightTf)))
terms_df <- orderBy(~ -Tf, data.frame(term=dimnames(terms_mtrx)$Terms,
Tf=rowSums(terms_mtrx)))
cmpnd_df <- subset(terms_df, grepl("-", term))
if (nrow(cmpnd_df) == 0) {
print(" No compounded terms found")
return(FALSE)
}
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_df[!cmpnd_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_df[!cmpnd_df$filter, "term"], ignore.case=TRUE)
cmpnd_df <- subset(cmpnd_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_df): %d", nrow(cmpnd_df)))
myprint_df(cmpnd_df)
}
# This should be run after glb_txt_corpus_lst is created with tolower
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
find_cmpnd_wrds(txt_vctr=glb_txt_chr_lst[[txt_var]])
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_txt_terms <- function(terms_TDM) {
terms_mtrx <- as.matrix(as.TermDocumentMatrix(terms_TDM))
terms_df <- data.frame(term=dimnames(terms_mtrx)$Terms, weight=rowSums(terms_mtrx),
freq=rowSums(terms_mtrx > 0))
terms_df$pos <- 1:nrow(terms_df)
terms_df$cor.y <-
cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df$.src == "Train",],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use="pairwise.complete.obs")
terms_df$cor.y.abs <- abs(terms_df$cor.y)
for (cls in unique(glb_allobs_df[, glb_txt_cor_var])) {
if (!is.na(cls))
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(!is.na(glb_allobs_df[, glb_txt_cor_var]) &
(glb_allobs_df[, glb_txt_cor_var] == cls))) else
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(is.na(glb_allobs_df[, glb_txt_cor_var])))
}
# Check all calls to get_terms_DTM_terms to change returned order assumption
return(terms_df <- orderBy(~ -weight, terms_df))
}
#plt_full_df <- get_terms_DTM_terms(terms_DTM=glb_full_terms_DTM_lst[[txt_var]])
get_corpus_terms <- function(txt_corpus) {
return(terms_df <- get_txt_terms(terms_TDM=TermDocumentMatrix(txt_corpus,
control=glb_txt_terms_control)))
}
#stop(here"); glb_to_sav()
glb_txt_corpus_lst <- list()
print(sprintf("Building glb_txt_corpus_lst..."))
glb_txt_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_chr_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
if (!is.null(glb_txt_stop_words[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, removeWords, glb_txt_stop_words[[txt_var]],
lazy=FALSE)#, lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_txt_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_txt_corpus_lst) <- glb_txt_vars
mycombineSynonyms <- content_transformer(function(x, syn=NULL) {
Reduce(function(a,b) {
gsub(paste0("\\b(", paste(b$syns, collapse="|"),")\\b"), b$word, a)}, syn, x)
})
#stop(here"); glb_to_sav(); sav_txt_corpus <- glb_txt_corpus_lst[[txt_var]]; all.equal(sav_txt_corpus, glb_txt_corpus_lst[[txt_var]]); glb_txt_corpus_lst[[txt_var]] <- sav_txt_corpus
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_terms_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_terms_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop term weights for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_terms_df <-
get_corpus_terms(txt_corpus=glb_txt_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_terms_mtrx_lst[[txt_var]] <- terms_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.post.stop.n <- rowSums(terms_stop_mtrx > 0)
tmp_allobs_df$terms.post.stop.n.log <- log(1 + tmp_allobs_df$terms.post.stop.n)
tmp_allobs_df$weight.post.stop.sum <- rowSums(terms_stop_mtrx)
print(sprintf(" Top_n stem term weights for %s:", txt_var))
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]], stemDocument,
"english", lazy=FALSE)
if (!is.null(glb_txt_synonyms[[txt_var]])) {
syn_lst <- myrmNullObj(glb_txt_synonyms[[txt_var]])
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]],
mycombineSynonyms,
syn_lst, lazy=FALSE)
}
print(myprint_df(full_terms_df <- get_corpus_terms(glb_txt_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_terms_mtrx_lst[[txt_var]] <- terms_stem_mtrx
tmp_allobs_df$terms.post.stem.n <- rowSums(terms_stem_mtrx > 0)
tmp_allobs_df$terms.post.stem.n.log <- log(1 + tmp_allobs_df$terms.post.stem.n)
tmp_allobs_df$weight.post.stem.sum <- rowSums(terms_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.post.stem.n / tmp_allobs_df$terms.post.stop.n
tmp_allobs_df[(is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$terms.n.stem.stop.Ratio)),
"terms.n.stem.stop.Ratio"] <- 1.0
if ((n.errors <- sum(tmp_allobs_df$terms.n.stem.stop.Ratio > 1)) > 0)
stop(n.errors, " obs in tmp_allobs_df have terms.n.stem.stop.Ratio > 1",
" happening due to terms filtered by glb_txt_terms_control$bounds$global[1] but stemmable to other terms")
#print(head(subset(tmp_allobs_df, terms.n.stem.stop.Ratio > 1)))
#glb_allobs_df[(row_ix <- which(glb_allobs_df$UniqueID == 10465)), ]
#terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]
#setdiff(names(terms_stem_mtrx[row_ix, terms_stem_mtrx[row_ix, ] > 0]), names(terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]))
#mydsp_obs(list(descr.my.contains="updat"))
tmp_allobs_df$weight.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$weight.post.stem.sum / tmp_allobs_df$weight.post.stop.sum
tmp_allobs_df[is.nan(tmp_allobs_df$weight.sum.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$weight.sum.stem.stop.Ratio),
"weight.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(paste0(txt_var_pfx, ".terms.post."), c("stop.n", "stem.n"), sep=""))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting term weights for %s...", txt_var))
txt_corpus <- glb_txt_corpus_lst[[txt_var]]
full_DTM <- DocumentTermMatrix(txt_corpus,
control=glb_txt_terms_control)
sprs_DTM <- removeSparseTerms(full_DTM,
glb_sprs_thresholds[txt_var])
glb_full_DTM_lst[[txt_var]] <- full_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting term weights for %s...", txt_var))
full_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_DTM)
full_terms_df <- get_txt_terms(full_DTM)
# full_terms_df <- full_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(full_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".full", sep="")
# names(full_terms_df) <- col_names
print(" Sparse TermMatrix:"); print(sprs_DTM)
sprs_terms_df <- get_txt_terms(sprs_DTM)
# sprs_terms_df <- sprs_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(sprs_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".sprs", sep="")
# names(sprs_terms_df) <- col_names
intersect(names(full_terms_df), names(sprs_terms_df))
terms_df <- merge(full_terms_df, sprs_terms_df,
by=setdiff(intersect(names(full_terms_df), names(sprs_terms_df)), "pos"),
all.x=TRUE, suffixes=c(".full", ".sprs"))
terms_df$in.sprs <- !is.na(terms_df$pos.sprs)
plt_terms_df <- subset(terms_df,
weight >= min(terms_df$weight[!is.na(terms_df$pos.sprs)], na.rm=TRUE))
plt_terms_df$label <- ""
plt_terms_df[is.na(plt_terms_df$pos.sprs), "label"] <-
plt_terms_df[is.na(plt_terms_df$pos.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_terms_df[is.na(plt_terms_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_terms_df, "freq", "weight",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_terms_df <- orderBy(~ -value,
melt(terms_df, id.vars="term", measure.vars = c("weight", "freq")))
print(ggplot(melt_terms_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(melt_terms_df, "term", "value", colorcol_name="variable"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, !in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(head(melt_terms_df, glb_txt_top_n[[txt_var]]), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# print(identical(sav_glb_txt_corpus_lst, glb_txt_corpus_lst))
# print(all.equal(length(sav_glb_txt_corpus_lst), length(glb_txt_corpus_lst)))
# print(all.equal(names(sav_glb_txt_corpus_lst), names(glb_txt_corpus_lst)))
# print(all.equal(sav_glb_txt_corpus_lst[["Headline"]], glb_txt_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
rm(full_terms_mtrx)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
require(tidyr)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_txt_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
plt_full_df <- terms_full_df
names(plt_full_df)[grepl("weight$", names(plt_full_df))] <- "weight.all"
# gather(plt_full_df[1:5, ], domain, TfIdf, -matches("!(TfIdf)"))
# gather(plt_full_df[1:5, grepl("TfIdf", names(plt_full_df))], domain, TfIdf)
# gather(plt_full_df[1:5, ], domain, TfIdf,
# -names(plt_full_df)[!grepl("TfIdf", names(plt_full_df))])
plt_full_df <- gather(plt_full_df, domain, weight,
-c(term, freq, pos, cor.y, cor.y.abs))
plt_full_df$label <- NA
top_val_terms <- orderBy(~-weight, terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
plt_full_df[plt_full_df$term %in% top_val_terms, "label"] <-
plt_full_df[plt_full_df$term %in% top_val_terms, "term"]
top_cor_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
plt_full_df[plt_full_df$term %in% top_cor_terms, "label"] <-
plt_full_df[plt_full_df$term %in% top_cor_terms, "term"]
#plt_full_df$type <- "none"
plt_full_df[plt_full_df$term %in% top_val_terms, "type"] <- "top.weight"
plt_full_df[plt_full_df$term %in% top_cor_terms, "type"] <- "top.cor"
plt_full_df[plt_full_df$term %in% intersect(top_val_terms, top_cor_terms), "type"] <-
"top.both"
cor.y.rnorm <- cor(glb_allobs_df$.rnorm, glb_allobs_df[, glb_rsp_var],
use="pairwise.complete.obs")
print(ggplot(plt_full_df, aes(x=weight, y=cor.y)) + facet_wrap(~ domain) +
geom_point(aes(size=freq), color="grey") +
geom_jitter() +
geom_text(aes(label=label, color=type), size=3.5) +
#geom_hline(yintercept=cor.y.rnorm, color="red") +
geom_hline(yintercept=c(cor.y.rnorm, -cor.y.rnorm), color="red"))
if (glb_txt_terms_filter == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
select_terms <- make.names(colnames(txt_X_df))
# colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
# make.names(colnames(txt_X_df)), sep="")
# rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_txt_terms_filter == "top.val") {
select_terms <- orderBy(~-weight,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glb_txt_terms_filter == "top.cor") {
select_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glb_txt_terms_filter == "union.top.val.cor") {
select_terms <- union(
orderBy(~-weight , terms_full_df)$term[1:glb_txt_top_n[[txt_var]]],
orderBy(~-cor.y.abs, terms_full_df)$term[1:glb_txt_top_n[[txt_var]]])
} else stop(
"glb_txt_terms_filter should be one of c('sparse', 'top.val', 'top.cor', 'union.top.val.cor') vs. '",
glb_txt_terms_filter, "'")
assoc_terms_lst <- findAssocs(glb_full_DTM_lst[[txt_var]], select_terms,
glb_txt_assoc_cor[[txt_var]])
assoc_terms <- c(NULL)
for (term in names(assoc_terms_lst))
if (length(assoc_terms_lst[[term]]) > 0)
assoc_terms <- union(assoc_terms, names(assoc_terms_lst[[term]]))
txt_X_df <- txt_full_X_df[,
subset(terms_full_df, term %in% c(select_terms, assoc_terms))$pos,
FALSE]
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "\\^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.wrds.n.log & .wrds.unq.n.log
txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".wrds.unq.n.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <-
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")]),
paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 0
# Create <txt_var>.chrs.n.log
txt_X_df[, paste0(txt_var_pfx, ".chrs.n.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".chrs.uppr.n.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".dgts.n.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".chrs.pnct", sprintf("%02d", punct_ix), ".n.log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.wrds.stop.n.log & <txt_var>ratio.wrds.stop.n.wrds.n
if (!is.null(glb_txt_stop_words[[txt_var]])) {
stop_words_rex_str <- paste0("\\b(",
paste0(glb_txt_stop_words[[txt_var]], collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.wrds.stop.n.wrds.n")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".wrds.n", ".log")])
}
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr## label step_major step_minor label_minor
## 2 extract.features_factorize.str.vars 2 0 0
## 3 extract.features_process.text 3 0 0
## bgn end elapsed
## 2 45.740 45.822 0.082
## 3 45.822 NA NA
## [1] "Building glb_txt_chr_lst..."
## [1] "running gsub for 10 (of 179): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 179): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 179): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 179): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 179): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 179): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 179): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 179): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 179): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 179): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 179): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 179): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 179): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 179): #\\bWall Street#..."
## [1] "running gsub for 150 (of 179): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 179): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 179): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 5
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 ONLY. 5
## 2 GB. 4
## 3 CONDITION. 3
## 4 BOX. 2
## 5 ESN. 2
## 6 GOOD. 2
## 7 IPADS. 2
## 8 TEAR. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 5 New Condition 1
## 6 New Digitizer 1
## 7 New Opened 1
## 8 New Scratch 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed
## 3 0 0 45.822 46.999 1.177
## 4 1 1 47.000 NA NA
## [1] "Remaining compound terms in descr.my: "
## [1] " No compounded terms found"
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor label_minor bgn end elapsed
## 4 1 1 47.000 49.005 2.005
## 5 0 0 49.006 NA NA
## [1] "Building glb_txt_corpus_lst..."
## [1] " Top_n stop term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero## [1] "Rows: 178; Cols: 9"
## term weight freq pos cor.y cor.y.abs
## condition condition 125.47296 335 30 0.035808430 0.035808430
## new new 86.04035 100 101 0.052272261 0.052272261
## used used 81.75453 141 169 0.038988418 0.038988418
## good good 77.72398 130 64 -0.020868951 0.020868951
## scratches scratches 72.48889 168 136 0.023946371 0.023946371
## screen screen 68.23607 127 137 0.009868377 0.009868377
## weight.(2.16,3] weight.(1.32,2.16] weight.(0.473,1.32]
## condition 104.69372 20.47912 0.3001182
## new 73.78521 12.25514 0.0000000
## used 64.70479 17.04974 0.0000000
## good 55.50939 21.71938 0.4952109
## scratches 58.85032 13.19621 0.4423610
## screen 43.08176 25.15430 0.0000000
## term weight freq pos cor.y cor.y.abs
## condition condition 125.47296 335 30 0.035808430 0.035808430
## affect affect 19.61869 24 7 0.005925489 0.005925489
## order order 14.55875 14 109 -0.018883490 0.018883490
## restored restored 12.34922 9 132 -0.050425513 0.050425513
## dings dings 11.22703 12 47 -0.008054097 0.008054097
## bottom bottom 7.56412 5 19 0.006360932 0.006360932
## weight.(2.16,3] weight.(1.32,2.16] weight.(0.473,1.32]
## condition 104.69372 20.479123 0.3001182
## affect 15.12050 4.498196 0.0000000
## order 11.64488 2.913872 0.0000000
## restored 8.99472 3.354505 0.0000000
## dings 8.04357 3.183457 0.0000000
## bottom 7.56412 0.000000 0.0000000
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## several several 3.547737 4 143 -0.06320792 0.06320792 2.486631
## missing missing 3.442698 4 97 -0.05342836 0.05342836 2.499493
## else else 2.992694 3 50 -0.10586673 0.10586673 0.000000
## items items 2.967961 3 75 NA NA 2.967961
## means means 2.967961 3 92 0.01432865 0.01432865 2.967961
## scuff scuff 2.374368 3 139 0.06496245 0.06496245 2.374368
## weight.(1.32,2.16] weight.(0.473,1.32]
## several 1.0611055 0
## missing 0.9432049 0
## else 2.9926936 0
## items 0.0000000 0
## means 0.0000000 0
## scuff 0.0000000 0
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## several several 3.547737 4 143 -0.06320792 0.06320792 2.486631
## missing missing 3.442698 4 97 -0.05342836 0.05342836 2.499493
## else else 2.992694 3 50 -0.10586673 0.10586673 0.000000
## items items 2.967961 3 75 NA NA 2.967961
## means means 2.967961 3 92 0.01432865 0.01432865 2.967961
## scuff scuff 2.374368 3 139 0.06496245 0.06496245 2.374368
## weight.(1.32,2.16] weight.(0.473,1.32]
## several 1.0611055 0
## missing 0.9432049 0
## else 2.9926936 0
## items 0.0000000 0
## means 0.0000000 0
## scuff 0.0000000 0## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero## [1] "Rows: 160; Cols: 9"
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## condit condit 125.47296 335 30 0.03580843 0.03580843 104.69372
## use use 95.56784 169 153 0.02167331 0.02167331 76.15142
## new new 86.04035 100 92 0.05227226 0.05227226 73.78521
## scratch scratch 79.97880 180 124 0.01416983 0.01416983 64.15626
## good good 77.72398 130 59 -0.02086895 0.02086895 55.50939
## work work 75.40786 124 160 -0.20145080 0.20145080 51.88958
## weight.(1.32,2.16] weight.(0.473,1.32]
## condit 20.47912 0.3001182
## use 19.41641 0.0000000
## new 12.25514 0.0000000
## scratch 15.39439 0.4281416
## good 21.71938 0.4952109
## work 21.75096 1.7673240
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## crack crack 41.513898 40 34 -0.03880692 0.03880692 22.984762
## lock lock 32.141461 18 77 -0.10622132 0.10622132 5.208321
## air air 16.289874 12 8 -0.10075327 0.10075327 15.303605
## packag packag 15.182304 16 106 0.04733164 0.04733164 14.461321
## lcd lcd 6.416863 5 71 -0.01890689 0.01890689 5.395998
## bezel bezel 4.914771 4 15 -0.09929314 0.09929314 3.971566
## weight.(1.32,2.16] weight.(0.473,1.32]
## crack 18.5291358 0
## lock 26.9331400 0
## air 0.9862688 0
## packag 0.7209827 0
## lcd 1.0208645 0
## bezel 0.9432049 0
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## regular regular 4.025712 5 119 0.018625060 0.018625060 3.209021
## left left 4.006747 3 73 0.006360932 0.006360932 4.006747
## sever sever 3.547737 4 130 -0.063207924 0.063207924 2.486631
## miss miss 3.442698 4 88 -0.053428356 0.053428356 2.499493
## els els 2.992694 3 46 -0.105866734 0.105866734 0.000000
## mean mean 2.967961 3 83 0.014328655 0.014328655 2.967961
## weight.(1.32,2.16] weight.(0.473,1.32]
## regular 0.8166916 0
## left 0.0000000 0
## sever 1.0611055 0
## miss 0.9432049 0
## els 2.9926936 0
## mean 0.0000000 0
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## regular regular 4.025712 5 119 0.018625060 0.018625060 3.209021
## left left 4.006747 3 73 0.006360932 0.006360932 4.006747
## sever sever 3.547737 4 130 -0.063207924 0.063207924 2.486631
## miss miss 3.442698 4 88 -0.053428356 0.053428356 2.499493
## els els 2.992694 3 46 -0.105866734 0.105866734 0.000000
## mean mean 2.967961 3 83 0.014328655 0.014328655 2.967961
## weight.(1.32,2.16] weight.(0.473,1.32]
## regular 0.8166916 0
## left 0.0000000 0
## sever 1.0611055 0
## miss 0.9432049 0
## els 2.9926936 0
## mean 0.0000000 0## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.post.stop.n -0.09961391
## terms.post.stop.n.log -0.10967396
## weight.post.stop.sum -0.12811319
## terms.post.stem.n -0.09976074
## terms.post.stem.n.log -0.10971057
## weight.post.stem.sum -0.12529621
## terms.n.stem.stop.Ratio 0.02161360
## weight.sum.stem.stop.Ratio 0.08928728
## label step_major step_minor label_minor bgn
## 5 extract.features_build.corpus 4 0 0 49.006
## 6 extract.features_extract.DTM 5 0 0 54.873
## end elapsed
## 5 54.872 5.866
## 6 NA NA
## [1] "Extracting term weights for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor label_minor bgn
## 6 extract.features_extract.DTM 5 0 0 54.873
## 7 extract.features_report.DTM 6 0 0 55.740
## end elapsed
## 6 55.74 0.867
## 7 NA NA## Loading required package: reshape2## [1] "Reporting term weights for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 1437, terms: 160)>>
## Non-/sparse entries: 4112/225808
## Sparsity : 98%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 1437, terms: 12)>>
## Non-/sparse entries: 1583/15661
## Sparsity : 91%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_terms_df, "freq", "weight", colorcol_name =
## "in.sprs"): converting in.sprs to class:factor
## Warning in rm(full_terms_mtrx): object 'full_terms_mtrx' not found## label step_major step_minor label_minor bgn
## 7 extract.features_report.DTM 6 0 0 55.740
## 8 extract.features_bind.DTM 7 0 0 58.042
## end elapsed
## 7 58.042 2.302
## 8 NA NA## Loading required package: tidyr## [1] "Binding DTM for descr.my..."## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero
## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 488 rows containing missing values (geom_text).## label step_major step_minor label_minor bgn
## 8 extract.features_bind.DTM 7 0 0 58.042
## 9 extract.features_bind.DXM 8 0 0 59.744
## end elapsed
## 8 59.743 1.701
## 9 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# Use model info provided in description
# mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
#
# mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
#
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.mini > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])
# glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad mini"
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.air > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])
# #glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
# glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad Air"
# print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
# c(glb_id_var, "sold",
# "prdline.my", "color", "condition", "cellular", "carrier", "storage"
# #, "descr.my"
# )])
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
# tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
# names(tmp_allobs_df) <- "old.prdline.my"
# glb_allobs_df$prdline.my <-
# plyr::revalue(glb_allobs_df$prdline.my, c(
# # "iPad 1" = "iPad",
# # "iPad 2" = "iPad2+",
# "iPad 3" = "iPad 3+",
# "iPad 4" = "iPad 3+",
# "iPad 5" = "iPad 3+",
#
# "iPad Air" = "iPadAir",
# "iPad Air 2" = "iPadAir",
#
# "iPad mini" = "iPadmini",
# "iPad mini 2" = "iPadmini 2+",
# "iPad mini 3" = "iPadmini 2+",
# "iPad mini Retina" = "iPadmini 2+"
# ))
# tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))
# print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
# c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
# c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
# c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
# c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
# "White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
# glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# glb_allobs_df %>%
# unite(prdl.descr.my, c(productline, as.numeric(D.chrs.n.log > 0), sep="#"))
# unite_("prdl.descr.my", interp(~c("productline", as.numeric(D.chrs.n.log > 0), sep="#")))
glb_allobs_df$prdl.descr.my.fctr <- as.factor(paste(gsub(" ", "", glb_allobs_df$productline),
as.numeric(nchar(glb_allobs_df$description) > 0),
sep="#"))
print(table(glb_allobs_df$prdl.descr.my.fctr, glb_allobs_df$sold, useNA = "ifany"))##
## 0 1 <NA>
## Unknown#0 52 15 31
## Unknown#1 42 11 23
## iPad1#0 41 14 16
## iPad1#1 37 15 22
## iPad2#0 41 13 31
## iPad2#1 73 19 53
## iPad3#0 16 3 12
## iPad3#1 43 14 20
## iPad4#0 24 11 18
## iPad4#1 50 8 24
## iPadAir#0 41 12 19
## iPadAir#1 48 5 19
## iPadAir2#0 64 15 28
## iPadAir2#1 21 5 8
## iPadmini#0 71 20 26
## iPadmini#1 37 13 26
## iPadmini2#0 30 11 14
## iPadmini2#1 16 4 8
## iPadmini3#0 45 8 18
## iPadmini3#1 6 1 6print(mycreate_sqlxtab_df(glb_allobs_df, c("prdl.descr.my.fctr", "sold")))## prdl.descr.my.fctr sold .n
## 1 iPad2#1 0 73
## 2 iPadmini#0 0 71
## 3 iPadAir2#0 0 64
## 4 iPad2#1 NA 53
## 5 Unknown#0 0 52
## 6 iPad4#1 0 50
## 7 iPadAir#1 0 48
## 8 iPadmini3#0 0 45
## 9 iPad3#1 0 43
## 10 Unknown#1 0 42
## 11 iPad1#0 0 41
## 12 iPad2#0 0 41
## 13 iPadAir#0 0 41
## 14 iPad1#1 0 37
## 15 iPadmini#1 0 37
## 16 Unknown#0 NA 31
## 17 iPad2#0 NA 31
## 18 iPadmini2#0 0 30
## 19 iPadAir2#0 NA 28
## 20 iPadmini#0 NA 26
## 21 iPadmini#1 NA 26
## 22 iPad4#0 0 24
## 23 iPad4#1 NA 24
## 24 Unknown#1 NA 23
## 25 iPad1#1 NA 22
## 26 iPadAir2#1 0 21
## 27 iPad3#1 NA 20
## 28 iPadmini#0 1 20
## 29 iPad2#1 1 19
## 30 iPadAir#0 NA 19
## 31 iPadAir#1 NA 19
## 32 iPad4#0 NA 18
## 33 iPadmini3#0 NA 18
## 34 iPad1#0 NA 16
## 35 iPad3#0 0 16
## 36 iPadmini2#1 0 16
## 37 Unknown#0 1 15
## 38 iPad1#1 1 15
## 39 iPadAir2#0 1 15
## 40 iPad1#0 1 14
## 41 iPad3#1 1 14
## 42 iPadmini2#0 NA 14
## 43 iPad2#0 1 13
## 44 iPadmini#1 1 13
## 45 iPad3#0 NA 12
## 46 iPadAir#0 1 12
## 47 Unknown#1 1 11
## 48 iPad4#0 1 11
## 49 iPadmini2#0 1 11
## 50 iPad4#1 1 8
## 51 iPadAir2#1 NA 8
## 52 iPadmini2#1 NA 8
## 53 iPadmini3#0 1 8
## 54 iPadmini3#1 NA 6
## 55 iPadmini3#1 0 6
## 56 iPadAir#1 1 5
## 57 iPadAir2#1 1 5
## 58 iPadmini2#1 1 4
## 59 iPad3#0 1 3
## 60 iPadmini3#1 1 1glb_category_var <- "prdl.descr.my.fctr"
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
if (!is.null(glb_price_vars)) {
for (var in glb_price_vars) {
for (digit in 1:(log10(max(glb_allobs_df[, var], na.rm=TRUE)) + 1)) {
glb_allobs_df[, paste0(var, ".dgt", digit, ".is9")] <-
as.numeric(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
(10 ^ (digit - 1))) == 9)
}
for (decimal in 1:2) {
glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9")] <-
as.numeric(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
}
}
#as.numeric((as.integer(startprice) %% 10) == 9)
}
rm(corpus_lst
, glb_sprs_DTM_lst #, glb_full_DTM_lst
, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr): object
## 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor label_minor bgn
## 9 extract.features_bind.DXM 8 0 0 59.744
## 10 extract.features_end 9 0 0 77.860
## end elapsed
## 9 77.86 18.116
## 10 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 4 extract.features_process.text_reporting_compound_terms 3
## 8 extract.features_bind.DTM 7
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## step_minor label_minor bgn end elapsed duration
## 9 0 0 59.744 77.860 18.116 18.116
## 5 0 0 49.006 54.872 5.866 5.866
## 7 0 0 55.740 58.042 2.302 2.302
## 4 1 1 47.000 49.005 2.005 2.005
## 8 0 0 58.042 59.743 1.701 1.701
## 3 0 0 45.822 46.999 1.177 1.177
## 6 0 0 54.873 55.740 0.867 0.867
## 2 0 0 45.740 45.822 0.082 0.082
## 1 0 0 45.723 45.740 0.017 0.017
## [1] "Total Elapsed Time: 77.86 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 5 extract.features 3 0 0 45.715 79.203 33.488
## 6 cluster.data 4 0 0 79.203 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 6 cluster.data 4 0 0 79.203 81.216
## 7 manage.missing.data 4 1 1 81.216 NA
## elapsed
## 6 2.013
## 7 NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 422
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 798
## cellular.fctr D.terms.post.stop.n
## 801 767
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 767 767
## D.terms.post.stem.n D.terms.post.stem.n.log
## 767 767
## D.weight.post.stem.sum D.T.condit
## 767 1102
## D.T.use D.T.new
## 1268 1337
## D.T.scratch D.T.good
## 1257 1307
## D.T.work D.T.screen
## 1313 1310
## D.T.great D.T.box
## 1366 1365
## D.T.no D.T.excel
## 1354 1384
## D.T.like D.T.item
## 1388 1352
## D.T.cosmet D.T.minor
## 1345 1351
## D.T.crack D.T.perfect
## 1397 1401
## D.T.fulli D.T.function.
## 1385 1377
## D.T.hous D.T.origin
## 1366 1391
## D.T.will D.T.X100
## 1406 1379
## D.T.damag D.T.open
## 1420 1419
## D.T.tab D.T.list
## 1410 1402
## D.T.affect D.wrds.n.log
## 1413 760
## D.wrds.unq.n.log D.weight.sum
## 767 767
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 767 760
## D.chrs.uppr.n.log D.dgts.n.log
## 762 1283
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1397 1437
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1420 1437
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1379 1370
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1437 1378
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1422 1428
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1235 1370
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1006 1383
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1423 1365
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1437 1436
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1437 1437
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1437 1437
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1437 1437
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1437 1437
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1437 1429
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1437 1437
## D.wrds.stop.n.log D.P.http
## 829 1437
## D.P.mini D.P.air
## 1418 1425
## D.P.black D.P.white
## 1433 1433
## D.P.gold D.P.spacegray
## 1436 1434
## startprice.dgt1.is9 startprice.dgt2.is9
## 871 1226
## startprice.dgt3.is9 startprice.dcm1.is9
## 1432 878
## startprice.dcm2.is9
## 996
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 760 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 760# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 422
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 798
## cellular.fctr D.terms.post.stop.n
## 801 767
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 767 767
## D.terms.post.stem.n D.terms.post.stem.n.log
## 767 767
## D.weight.post.stem.sum D.T.condit
## 767 1102
## D.T.use D.T.new
## 1268 1337
## D.T.scratch D.T.good
## 1257 1307
## D.T.work D.T.screen
## 1313 1310
## D.T.great D.T.box
## 1366 1365
## D.T.no D.T.excel
## 1354 1384
## D.T.like D.T.item
## 1388 1352
## D.T.cosmet D.T.minor
## 1345 1351
## D.T.crack D.T.perfect
## 1397 1401
## D.T.fulli D.T.function.
## 1385 1377
## D.T.hous D.T.origin
## 1366 1391
## D.T.will D.T.X100
## 1406 1379
## D.T.damag D.T.open
## 1420 1419
## D.T.tab D.T.list
## 1410 1402
## D.T.affect D.wrds.n.log
## 1413 760
## D.wrds.unq.n.log D.weight.sum
## 767 767
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 767 760
## D.chrs.uppr.n.log D.dgts.n.log
## 762 1283
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1397 1437
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1420 1437
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1379 1370
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1437 1378
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1422 1428
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1235 1370
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1006 1383
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1423 1365
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1437 1436
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1437 1437
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1437 1437
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1437 1437
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1437 1437
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1437 1429
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1437 1437
## D.wrds.stop.n.log D.P.http
## 829 1437
## D.P.mini D.P.air
## 1418 1425
## D.P.black D.P.white
## 1433 1433
## D.P.gold D.P.spacegray
## 1436 1434
## startprice.dgt1.is9 startprice.dgt2.is9
## 871 1226
## startprice.dgt3.is9 startprice.dcm1.is9
## 1432 878
## startprice.dcm2.is9
## 996
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 760 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 760Step 4.1: manage missing data
mycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {
require(lazyeval)
require(dplyr)
require(tidyr)
if (is.null(by_var)) {
by_var <- ".default"
obs_df$.default <- as.factor(".default")
}
if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
obs_df$.clusterid <- 1
cluster_df <- obs_df %>%
count_(c(by_var, ".clusterid", entropy_var)) %>%
dplyr::filter(n > 0) %>%
dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
unite_(paste0(by_var, ".clusterid"),
c(interp(by_var), ".clusterid")) %>%
spread_(interp(entropy_var), "n", fill=0)
# head(cluster_df)
# sum(cluster_df$n)
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
#print(cluster_df)
return(cluster_df)
}
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
if (!inherits(x, "matrix"))
x <- as.matrix(x)
if (is.null(weights))
weights <- rep(1, ncol(x))
wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow=nrow(x), byrow=TRUE)
wgtdx <- x * wgtsx
wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
ret_mtrx[is.nan(ret_mtrx)] <- 1
diag(ret_mtrx) <- 0
return(ret_mtrx)
}
#pr_DB$delete_entry("mywgtdcosine");
# Need to do this only once across runs ?
if (!pr_DB$entry_exists("mywgtdcosine")) {
pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
}
#pr_DB$get_entry("mywgtdcosine")
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE)
# Assign correlations with rsp_var as weights for cosine distance
print("Clustering features: ")
cluster_vars_df <- data.frame(abs.cor.y=abs(cor(
glb_allobs_df[glb_allobs_df$.src == "Train", cluster_vars],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var],
use="pairwise.complete.obs")))
print(tail(cluster_vars_df <- orderBy(~ abs.cor.y, subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
print(sprintf(" .rnorm cor: %0.4f",
cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var], use="pairwise.complete.obs")))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
method="ML")))
print(category_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)",
glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
grp_ids <- sort(unique(glb_allobs_df[, glb_category_var]))
glb_cluster_size_df_lst <- list()
for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next
# if (grep(grp, levels(grp_ids)) > 9) next
# if (grep(grp, levels(grp_ids)) != 10) next
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
#dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)],
method = "mywgtdcosine",
weights=cluster_vars_df$abs.cor.y)
# Custom distance functions return a crossdist object
#dstns_mtrx <- as.matrix(dstns_dist)
dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
dimnames=attr(dstns_dist, "dimnames"))
dstns_dist <- as.dist(dstns_mtrx)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
# print(dim(dstns_mtrx))
# print(sprintf("which.max: %d", which.max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
# print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
# print(dim(ctgry_allobs_df))
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
# Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
if (with(clusters,all.equal(height,sort(height))))
clusters$height <- round(clusters$height,6)
myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var],
lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
opt_minclustersize_df <- data.frame(minclustersize=nrow(ctgry_allobs_df),
entropy=entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
method="ML"))
for (minclustersize in
as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10, length=5))) {
clusterGroups <- cutreeDynamic(clusters, minClusterSize=minclustersize, method="tree",
deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
clusterGroups[clusterGroups == 0] <- 1
ctgry_allobs_df$.clusterid <- clusterGroups
ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df, glb_cluster_entropy_var)
opt_minclustersize_df <- rbind(opt_minclustersize_df,
data.frame(minclustersize=minclustersize,
entropy=weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
}
opt_minclustersize <-
opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
opt_minclustersize_df$.color <-
ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
"red", "blue")
print(ggplot(data=opt_minclustersize_df, mapping=aes(x=minclustersize, y=entropy)) +
geom_point(aes(color=.color)) + scale_color_identity() + guides(color = "none") +
geom_line())
glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
# select minclustersize that minimizes entropy
clusterGroups <- cutreeDynamic(clusters, minClusterSize=opt_minclustersize,
method="tree",
deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
}
#all.equal(sav_allobs_df_clusterid, glb_allobs_df$.clusterid)
print(cluster_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
# .clusterid.fctr is created automatically (probably ?) later
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, ".clusterid")
if (!is.null(glb_category_var))
# glb_interaction_only_feats_lst[ifelse(grepl("\\.fctr", glb_category_var),
# glb_category_var,
# paste0(glb_category_var, ".fctr"))] <-
# c(".clusterid.fctr")
glb_interaction_only_feats_lst[[".clusterid.fctr"]] <-
ifelse(grepl("\\.fctr", glb_category_var), glb_category_var,
paste0(glb_category_var, ".fctr"))
if (glb_exclude_cluster_vars_as_features)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy## [1] "Clustering features: "## Warning in cor(glb_allobs_df[glb_allobs_df$.src == "Train",
## cluster_vars], : the standard deviation is zero## abs.cor.y
## D.T.item 0.08169668
## D.P.black 0.08676619
## D.P.air 0.10075327
## D.T.great 0.14363084
## D.T.work 0.20145080
## [1] " .rnorm cor: -0.0112"
## [1] "glb_allobs_df Entropy: 0.5189"## Loading required package: lazyeval## Source: local data frame [20 x 5]
##
## prdl.descr.my.fctr.clusterid 0 1 .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 52 15 0.5317758 67
## 2 Unknown#1_1 42 11 0.5106887 53
## 3 iPad1#0_1 41 14 0.5672740 55
## 4 iPad1#1_1 37 15 0.6007684 52
## 5 iPad2#0_1 41 13 0.5519323 54
## 6 iPad2#1_1 73 19 0.5093116 92
## 7 iPad3#0_1 16 3 0.4361623 19
## 8 iPad3#1_1 43 14 0.5574652 57
## 9 iPad4#0_1 24 11 0.6224869 35
## 10 iPad4#1_1 50 8 0.4011899 58
## 11 iPadAir#0_1 41 12 0.5349082 53
## 12 iPadAir#1_1 48 5 0.3124648 53
## 13 iPadAir2#0_1 64 15 0.4860394 79
## 14 iPadAir2#1_1 21 5 0.4895519 26
## 15 iPadmini#0_1 71 20 0.5266297 91
## 16 iPadmini#1_1 37 13 0.5730569 50
## 17 iPadmini2#0_1 30 11 0.5815533 41
## 18 iPadmini2#1_1 16 4 0.5004024 20
## 19 iPadmini3#0_1 45 8 0.4243420 53
## 20 iPadmini3#1_1 6 1 0.4101163 7
## [1] "glb_allobs_df$prdl.descr.my.fctr Entropy: 0.5111 (98.4844 pct)"
## [1] "Category: Unknown#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 221 10525 1 Unknown#0 0 0 0
## 252 10611 1 Unknown#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 221 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 221 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 221 0 0 0 0 0 0
## 252 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 221 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 221 0 0 0 0 0
## 252 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 221 10525 1 Unknown#0 0 0 0
## 221.1 10525 1 Unknown#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 221 0 0 0 0 0 0 0
## 221.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 221 0 0 0 0 0 0
## 221.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 221 0 0 0 0 0 0
## 221.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 221 0 0 0 0 0 0 0
## 221.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 221 0 0 0 0 0 0
## 221.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: Unknown#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 231 10557 1 Unknown#1
## 418 10986 1 Unknown#1
## descr.my
## 231 condition is 9 out of 10
## 418 Brand- new only broke the shrinkwrap to make sure that it was for TMobile.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 231 1.050414 0 0.000000 0 0 0 0
## 418 0.000000 0 0.961247 0 0 0 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 231 0 0 0 0 0 0 0
## 418 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 231 0 0 0 0 0 0
## 418 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 231 0 0 0 0 0 0 0
## 418 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 231 0 0 0 0 0 0 0
## 418 0 0 0 0 0 0 0
## D.P.spacegray
## 231 0
## 418 0
## [1] "min distance(0.8132) pair:"
## UniqueID sold prdl.descr.my.fctr
## 744 11443 0 Unknown#1
## 1131 12148 <NA> Unknown#1
## descr.my
## 744 Have signs of wear. Includes all accessories except the remote control and the user guide. Work
## 1131 Tested, in good working condition.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 744 0.0000000 0 0 0 0.0000000 0.7069296 0
## 1131 0.5252068 0 0 0 0.8666191 0.8836620 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 744 0 0 0 0 0 0 0
## 1131 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 744 0 0 0 0 0 0
## 1131 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 744 0 0 0 0 0 0 0
## 1131 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 744 0 0 0 0 0 0 0
## 1131 0 0 0 0 0 0 0
## D.P.spacegray
## 744 0
## 1131 0
## [1] "Category: iPad1#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 8 10014 1 iPad1#0 0 0 0
## 156 10306 1 iPad1#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 8 0 0 0 0 0 0 0
## 156 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 8 0 0 0 0 0 0 0
## 156 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 8 0 0 0 0 0 0
## 156 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 8 0 0 0 0 0 0 0
## 156 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8 0 0 0 0 0
## 156 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 8 10014 1 iPad1#0 0 0 0
## 8.1 10014 1 iPad1#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 8 0 0 0 0 0 0 0
## 8.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 8 0 0 0 0 0 0 0
## 8.1 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 8 0 0 0 0 0 0
## 8.1 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 8 0 0 0 0 0 0 0
## 8.1 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8 0 0 0 0 0
## 8.1 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad1#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 21 10027 1 iPad1#1
## 26 10032 1 iPad1#1
## descr.my
## 21 FULLY FUNCTIONAL
## 26 In very good condition, does show sign of use but mostly had a case on at all times. Still has a
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 21 0.0000000 0.0000000 0 0 0.0000000 0 0
## 26 0.2626034 0.3859956 0 0 0.4333096 0 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 21 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 21 0 0 0 2.394202 2.290977 0
## 26 0 0 0 0.000000 0.000000 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 21 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 21 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0
## D.P.spacegray
## 21 0
## 26 0
## [1] "min distance(0.8194) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my
## 670 11345 0 iPad1#1 Used - Good Working Condition
## 1382 12553 <NA> iPad1#1 Power button does not work.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work
## 670 0.5252068 0.7719912 0 0 0.8666191 0.883662
## 1382 0.0000000 0.0000000 0 0 0.0000000 1.178216
## D.T.screen D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item
## 670 0 0 0 0 0 0 0
## 1382 0 0 0 0 0 0 0
## D.T.cosmet D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function.
## 670 0 0 0 0 0 0
## 1382 0 0 0 0 0 0
## D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab
## 670 0 0 0 0 0 0 0
## 1382 0 0 0 0 0 0 0
## D.T.list D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white
## 670 0 0 0 0 0 0 0
## 1382 0 0 0 0 0 0 0
## D.P.gold D.P.spacegray
## 670 0 0
## 1382 0 0
## [1] "Category: iPad2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 93 10131 1 iPad2#0 0 0 0
## 146 10259 1 iPad2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 93 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 93 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 93 0 0 0 0 0 0
## 146 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 93 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 93 0 0 0 0 0
## 146 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 93 10131 1 iPad2#0 0 0 0
## 93.1 10131 1 iPad2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 93 0 0 0 0 0 0 0
## 93.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 93 0 0 0 0 0 0
## 93.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 93 0 0 0 0 0 0
## 93.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 93 0 0 0 0 0 0 0
## 93.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 93 0 0 0 0 0 0
## 93.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 15 10021 1 iPad2#1
## 38 10045 1 iPad2#1
## descr.my
## 15 Crack on screen.
## 38 Great condition had a new battery installed 3 months ago by my local Apple Store so it works like
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 15 0.0000000 0 0.0000000 0 0 0.000000 1.75008
## 38 0.2626034 0 0.4806235 0 0 0.441831 0.00000
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 15 0.0000000 0 0 0 0.0000000 0 0
## 38 0.5423872 0 0 0 0.6092668 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 15 0 2.583458 0 0 0 0
## 38 0 0.000000 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 15 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 15 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0
## D.P.spacegray
## 15 0
## 38 0
## [1] "min distance(0.7997) pair:"
## UniqueID sold prdl.descr.my.fctr
## 895 11673 0 iPad2#1
## 1395 12598 <NA> iPad2#1
## descr.my
## 895 Pin locked, home button does not work.
## 1395 Touch screen on this iPad is not working. Home button does not seem to be working either. Selling AS
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 895 0 0 0 0 0 0.883662 0.0000000
## 1395 0 0 0 0 0 1.413859 0.7000319
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 895 0 0 0 0 0 0 0
## 1395 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 895 0 0 0 0 0 0
## 1395 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 895 0 0 0 0 0 0 0
## 1395 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 895 0 0 0 0 0 0 0
## 1395 0 0 0 0 0 0 0
## D.P.spacegray
## 895 0
## 1395 0
## [1] "Category: iPad3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 34 10041 1 iPad3#0 0 0 0
## 131 10218 1 iPad3#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 34 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 34 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 34 0 0 0 0 0 0
## 131 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 34 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 34 0 0 0 0 0
## 131 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 34 10041 1 iPad3#0 0 0 0
## 34.1 10041 1 iPad3#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 34 0 0 0 0 0 0 0
## 34.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 34 0 0 0 0 0 0
## 34.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 34 0 0 0 0 0 0
## 34.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 34 0 0 0 0 0 0 0
## 34.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 34 0 0 0 0 0 0
## 34.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 11 10017 1 iPad3#1
## 30 10037 1 iPad3#1
## descr.my
## 11 Great working iPad. Very minor surface scratches on back as pictures. Other very light scratches
## 30 Rarely ever used it.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 11 0 0.000000 0 0.6659981 0 0.3927387 0
## 30 0 1.543982 0 0.0000000 0 0.0000000 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 11 0.4821219 0 0 0 0 0 0
## 30 0.0000000 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 11 0.4513977 0 0 0 0 0
## 30 0.0000000 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 11 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 11 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0
## D.P.spacegray
## 11 0
## 30 0
## [1] "min distance(0.8305) pair:"
## UniqueID sold prdl.descr.my.fctr
## 16 10022 0 iPad3#1
## 310 10765 0 iPad3#1
## descr.my
## 16 Great working iPad. Only light scratches consistent with typical wear as pictures.
## 310 Works great -- One owner
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 16 0 0 0 0.4994985 0 0.589108 0
## 310 0 0 0 0.0000000 0 0.883662 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 16 0.7231829 0 0 0 0 0 0
## 310 1.0847743 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 16 0 0 0 0 0 0
## 310 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 16 0 0 0 0 0 0 0
## 310 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 16 0 0 0 0 0 0 0
## 310 0 0 0 0 0 0 0
## D.P.spacegray
## 16 0
## 310 0
## [1] "Category: iPad4#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 2 10003 1 iPad4#0 0 0 0
## 79 10105 1 iPad4#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 2 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 2 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag
## 2 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini D.P.air
## 2 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 2 0 0 0 0
## 79 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 2 10003 1 iPad4#0 0 0 0
## 2.1 10003 1 iPad4#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 2 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 2 0 0 0 0 0
## 2.1 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad4#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 6 10011 1 iPad4#1
## 49 10059 0 iPad4#1
## descr.my
## 6 good condition, minor wear and tear on body some light scratches on screen. functions great.
## 49 Will have some cosmetic damage.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 6 0.1909843 0 0 0.2724538 0.3151342 0 0.3181963
## 49 0.0000000 0 0 0.0000000 0.0000000 0 0.0000000
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 6 0.3944634 0 0 0 0 0 0.000000
## 49 0.0000000 0 0 0 0 0 1.321761
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 6 0.3693254 0 0 0 0.4165413 0
## 49 0.0000000 0 0 0 0.0000000 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 6 0 0.000000 0 0.000000 0 0 0
## 49 0 1.844883 0 2.133794 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 6 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0
## D.P.spacegray
## 6 0
## 49 0
## [1] "min distance(0.8243) pair:"
## UniqueID sold prdl.descr.my.fctr
## 1011 11857 1 iPad4#1
## 376 10915 0 iPad4#1
## descr.my
## 1011 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 376 Tablet is Good Working and has been reset to factory settings.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work
## 1011 0.2334252 0.3431072 0 0.332999 0.0000000 0.3927387
## 376 0.0000000 0.0000000 0 0.000000 0.6932953 0.7069296
## D.T.screen D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item
## 1011 0 0 0 0 0 0 0
## 376 0 0 0 0 0 0 0
## D.T.cosmet D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function.
## 1011 0 0 0 0 0 0
## 376 0 0 0 0 0 0
## D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab
## 1011 0 0 0 0 0 0 0
## 376 0 0 0 0 0 0 0
## D.T.list D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white
## 1011 0 0 0 0 0 0 0
## 376 0 0 0 0 0 0 0
## D.P.gold D.P.spacegray
## 1011 0 0
## 376 0 0
## [1] "Category: iPadAir#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 62 10080 1 iPadAir#0 0 0 0
## 82 10109 1 iPadAir#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 62 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 62 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag
## 62 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0
## D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini D.P.air
## 62 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 62 0 0 0 0
## 82 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 62 10080 1 iPadAir#0 0 0 0
## 62.1 10080 1 iPadAir#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 62 0 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 62 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 62 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 62 0 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 62 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 24 10030 1 iPadAir#1
## 117 10183 1 iPadAir#1
## descr.my
## 24 Comes with USB Cable and wall adapter. May have minor dings or scuffs.
## 117 It's in an open box with all accessories still in the original packaging.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 24 0 0 0 0 0 0 0
## 117 0 0 0 0 0 0 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 24 0 0.0000000 0 0 0 0 0
## 117 0 0.7198199 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 24 0.4513977 0 0 0 0 0
## 117 0.0000000 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 24 0.0000000 0 0 0 0.000000 0 0
## 117 0.8275471 0 0 0 1.053153 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 24 0 0 0 0 0 0 0
## 117 0 0 0 0 0 0 0
## D.P.spacegray
## 24 0
## 117 0
## [1] "min distance(0.8149) pair:"
## UniqueID sold prdl.descr.my.fctr
## 412 10980 0 iPadAir#1
## 1201 12312 <NA> iPadAir#1
## descr.my
## 412 Tablet is Good Working. Has been reset to the factory settings.
## 1201 Tablet is Good Working. Has been reset to the factory settings.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 412 0 0 0 0 0.6932953 0.7069296 0
## 1201 0 0 0 0 0.6932953 0.7069296 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 412 0 0 0 0 0 0 0
## 1201 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 412 0 0 0 0 0 0
## 1201 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 412 0 0 0 0 0 0 0
## 1201 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 412 0 0 0 0 0 0 0
## 1201 0 0 0 0 0 0 0
## D.P.spacegray
## 412 0
## 1201 0
## [1] "Category: iPadAir2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 13 10019 1 iPadAir2#0 0 0 0
## 42 10051 1 iPadAir2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 13 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 13 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag
## 13 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini D.P.air
## 13 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 13 0 0 0 0
## 42 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 13 10019 1 iPadAir2#0 0 0 0
## 13.1 10019 1 iPadAir2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 13 0 0 0 0 0 0 0
## 13.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 13 0 0 0 0 0 0
## 13.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 13 0 0 0 0 0 0
## 13.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 13 0 0 0 0 0 0 0
## 13.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 13 0 0 0 0 0 0
## 13.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 194 10436 1 iPadAir2#1
## 935 11759 1 iPadAir2#1
## descr.my
## 194 2 months old in perfect condition. Still under apple warranty. Black Apple Smart Case included.
## 935 iPad Air 2 16gb Logic board NO ICLOUD !!! NO TURNS ON , NO POWER
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 194 0.1750689 0 0 0 0 0 0
## 935 0.0000000 0 0 0 0 0 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 194 0 0 0.000000 0 0 0 0
## 935 0 0 1.763059 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 194 0 0 0.4432433 0 0 0
## 935 0 0 0.0000000 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 194 0 0 0 0 0 0 0
## 935 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 194 0 0 0 0 1 0 0
## 935 0 0 0 1 0 0 0
## D.P.spacegray
## 194 0
## 935 0
## [1] "min distance(0.8520) pair:"
## UniqueID sold prdl.descr.my.fctr
## 132 10219 0 iPadAir2#1
## 249 10605 0 iPadAir2#1
## descr.my
## 132 An item that has been restored to working order by the eBay seller or a third party not approved by
## 249 Working. Tested Wifi, touch, sound, buttons, camera, charge port. Scratched case. Light scratches on
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 132 0 0 0 0.0000000 0 0.883662 0
## 249 0 0 0 0.9989971 0 0.589108 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 132 0 0 0 0 0 1.019863 0
## 249 0 0 0 0 0 0.000000 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 132 0 0 0 0 0 0
## 249 0 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 132 0 0 0 0 0 0 0
## 249 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 132 0 0 0 0 0 0 0
## 249 0 0 0 0 0 0 0
## D.P.spacegray
## 132 0
## 249 0
## [1] "Category: iPadmini#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 87 10117 1 iPadmini#0 0 0 0
## 106 10159 1 iPadmini#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 87 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 87 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 87 0 0 0 0 0 0
## 106 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 87 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 87 0 0 0 0 0
## 106 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 87 10117 1 iPadmini#0 0 0 0
## 87.1 10117 1 iPadmini#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 87 0 0 0 0 0 0 0
## 87.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 87 0 0 0 0 0 0
## 87.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 87 0 0 0 0 0 0
## 87.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 87 0 0 0 0 0 0 0
## 87.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 87 0 0 0 0 0 0
## 87.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 59 10076 1 iPadmini#1
## 142 10248 1 iPadmini#1
## descr.my
## 59 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## 142 Note: Surface scratches on the back cover
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 59 0.2626034 0 0 0.0000000 0.4333096 0.883662 0
## 142 0.0000000 0 0 0.5993983 0.0000000 0.000000 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 59 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 59 0 0 0.6648649 0 0 0
## 142 0 0 0.0000000 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 59 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 59 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0
## D.P.spacegray
## 59 0
## 142 0
## [1] "min distance(0.8142) pair:"
## UniqueID sold prdl.descr.my.fctr
## 59 10076 1 iPadmini#1
## 67 10086 1 iPadmini#1
## descr.my
## 59 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## 67 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 59 0.2626034 0 0 0 0.4333096 0.883662 0
## 67 0.2626034 0 0 0 0.4333096 0.883662 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 59 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 59 0 0 0.6648649 0 0 0
## 67 0 0 0.6648649 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 59 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 59 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0
## D.P.spacegray
## 59 0
## 67 0
## [1] "Category: iPadmini2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 108 10161 1 iPadmini2#0 0 0 0
## 137 10234 1 iPadmini2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 108 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 108 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 108 0 0 0 0 0 0
## 137 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 108 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 108 0 0 0 0 0
## 137 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 108 10161 1 iPadmini2#0 0 0 0
## 108.1 10161 1 iPadmini2#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 108 0 0 0 0 0 0 0
## 108.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 108 0 0 0 0 0 0
## 108.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 108 0 0 0 0 0 0
## 108.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 108 0 0 0 0 0 0 0
## 108.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 108 0 0 0 0 0 0
## 108.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 76 10101 1 iPadmini2#1
## 953 11787 1 iPadmini2#1
## descr.my
## 76 This item is in Excellent cosmetic condition. It will not have any scratches on the screen. It may
## 953 There is a minor issue with the LCD backlight causing the display to have a slight wavy texture.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 76 0.2626034 0 0 0.3746239 0 0 0.43752
## 953 0.0000000 0 0 0.0000000 0 0 0.00000
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 76 0 0 0 0.5951155 0 0.5099317 0.4956603
## 953 0 0 0 0.0000000 0 0.0000000 0.0000000
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 76 0.000000 0 0 0 0 0
## 953 1.015645 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 76 0 0.691831 0 0 0 0 0
## 953 0 0.000000 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 76 0 0 0 0 0 0 0
## 953 0 0 0 0 0 0 0
## D.P.spacegray
## 76 0
## 953 0
## [1] "min distance(0.8587) pair:"
## UniqueID sold prdl.descr.my.fctr
## 212 10506 0 iPadmini2#1
## 941 11768 0 iPadmini2#1
## descr.my
## 212 Working. Tested wifi, sound, charge port, buttons. Scratched case. Light scratches on lcd. No
## 941 Cracked but works read first before buying
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 212 0 0 0 0.7492478 0 0.441831 0
## 941 0 0 0 0.0000000 0 1.767324 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 212 0 0 0.5142256 0 0 0 0
## 941 0 0 0.0000000 0 0 0 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 212 0 0.000000 0 0 0 0
## 941 0 2.583458 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 212 0 0 0 0 0 0 0
## 941 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 212 0 0 0 0 0 0 0
## 941 0 0 0 0 0 0 0
## D.P.spacegray
## 212 0
## 941 0
## [1] "Category: iPadmini3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 162 10319 1 iPadmini3#0 0 0 0
## 197 10453 1 iPadmini3#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 162 0 0 0 0 0 0 0
## 197 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack D.T.perfect
## 162 0 0 0 0 0 0 0
## 197 0 0 0 0 0 0 0
## D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will D.T.X100
## 162 0 0 0 0 0 0
## 197 0 0 0 0 0 0
## D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http D.P.mini
## 162 0 0 0 0 0 0 0
## 197 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 162 0 0 0 0 0
## 197 0 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 162 10319 1 iPadmini3#0 0 0 0
## 162.1 10319 1 iPadmini3#0 0 0 0
## D.T.scratch D.T.good D.T.work D.T.screen D.T.great D.T.box D.T.no
## 162 0 0 0 0 0 0 0
## 162.1 0 0 0 0 0 0 0
## D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor D.T.crack
## 162 0 0 0 0 0 0
## 162.1 0 0 0 0 0 0
## D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin D.T.will
## 162 0 0 0 0 0 0
## 162.1 0 0 0 0 0 0
## D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect D.P.http
## 162 0 0 0 0 0 0 0
## 162.1 0 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 162 0 0 0 0 0 0
## 162.1 0 0 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 508 11119 1 iPadmini3#1
## 104 10155 0 iPadmini3#1
## descr.my
## 508 4 of 5. This item is gently used. The item shows minor signs of wear and tear included scuff marks
## 104 This listing does not include a charger.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen
## 508 0 0.2573304 0 0 0 0 0
## 104 0 0.0000000 0 0 0 0 0
## D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet
## 508 0 0 0 0 0 0.6799089 0
## 104 0 0 0 0 0 0.0000000 0
## D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous
## 508 0.3385483 0 0 0 0 0
## 104 0.0000000 0 0 0 0 0
## D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list
## 508 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 508 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## D.P.spacegray
## 508 0
## 104 0
## [1] "min distance(0.8686) pair:"
## UniqueID sold prdl.descr.my.fctr
## 1239 12369 <NA> iPadmini3#1
## 1290 12430 <NA> iPadmini3#1
## descr.my
## 1239 Mint condition. Not a single scratch. I got this iPad mini 3 as a bonus from the company I work for
## 1290 Excellent condition. Under Applecare November 09, 2015. Working and tested. Minor sign of use.
## D.T.condit D.T.use D.T.new D.T.scratch D.T.good D.T.work
## 1239 0.4201654 0.0000000 0 0.5993983 0 0.7069296
## 1290 0.3001182 0.4411378 0 0.0000000 0 0.5049497
## D.T.screen D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item
## 1239 0 0 0 0 0.000000 0 0
## 1290 0 0 0 0 0.680132 0 0
## D.T.cosmet D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function.
## 1239 0 0.0000000 0 0 0 0
## 1290 0 0.5803685 0 0 0 0
## D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab
## 1239 0 0 0 0 0 0 0
## 1290 0 0 0 0 0 0 0
## D.T.list D.T.affect D.P.http D.P.mini D.P.air D.P.black D.P.white
## 1239 0 0 0 1 0 0 0
## 1290 0 0 0 0 0 0 0
## D.P.gold D.P.spacegray
## 1239 0 0
## 1290 0 0
## [1] "No module detected"
## Source: local data frame [42 x 5]
##
## prdl.descr.my.fctr.clusterid 0 1 .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 52 15 0.5317758 67
## 2 Unknown#1_1 24 7 0.5341594 31
## 3 Unknown#1_2 7 4 0.6554818 11
## 4 Unknown#1_3 11 0 0.0000000 11
## 5 iPad1#0_1 41 14 0.5672740 55
## 6 iPad1#1_1 9 11 0.6881388 20
## 7 iPad1#1_2 10 1 0.3046361 11
## 8 iPad1#1_3 9 1 0.3250830 10
## 9 iPad1#1_4 9 2 0.4741393 11
## 10 iPad2#0_1 41 13 0.5519323 54
## .. ... ... ... ... ...
## [1] "glb_allobs_df$prdl.descr.my.fctr$.clusterid Entropy: 0.4890 (95.6850 pct)"# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 7 manage.missing.data 4 1 1 81.216 94.321
## 8 partition.data.training 5 0 0 94.322 NA
## elapsed
## 7 13.106
## 8 NAStep 5.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for startprice.log10; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
if (!is.null(glb_obsfit_outliers)) {
glb_OOBobs_df <- rbind(glb_OOBobs_df,
glb_fitobs_df[glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers, ])
glb_fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers), ]
}
glb_allobs_df$.lcn <- ""; glb_trnobs_df$.lcn <- "";
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## prdl.descr.my.fctr .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 6 iPad2#1 126 126 0.103278689 0.10218978
## 15 iPadmini#0 97 97 0.079508197 0.07866991
## 13 iPadAir2#0 92 92 0.075409836 0.07461476
## 1 Unknown#0 83 86 0.068032787 0.06974858
## 10 iPad4#1 74 76 0.060655738 0.06163828
## 5 iPad2#0 72 73 0.059016393 0.05920519
## 12 iPadAir#1 67 68 0.054918033 0.05515004
## 2 Unknown#1 65 66 0.053278689 0.05352798
## 8 iPad3#1 63 64 0.051639344 0.05190592
## 16 iPadmini#1 63 64 0.051639344 0.05190592
## 19 iPadmini3#0 63 63 0.051639344 0.05109489
## 11 iPadAir#0 60 60 0.049180328 0.04866180
## 4 iPad1#1 59 61 0.048360656 0.04947283
## 3 iPad1#0 57 57 0.046721311 0.04622871
## 17 iPadmini2#0 44 44 0.036065574 0.03568532
## 9 iPad4#0 42 42 0.034426230 0.03406326
## 14 iPadAir2#1 29 30 0.023770492 0.02433090
## 7 iPad3#0 28 28 0.022950820 0.02270884
## 18 iPadmini2#1 24 24 0.019672131 0.01946472
## 20 iPadmini3#1 12 12 0.009836066 0.00973236print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 1437 113print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 217 113print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 204 112print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 1233 112print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1220 112# # Does not handle NULL or length(glb_id_var) > 1
if (glb_save_envir)
save(glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 8 partition.data.training 5 0 0 94.322 99.699
## 9 select.features 6 0 0 99.700 NA
## elapsed
## 8 5.377
## 9 NAStep 6.0: select features
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## startprice startprice 0.870529714
## startprice.log10.cut.fctr startprice.log10.cut.fctr 0.835918829
## prdl.descr.my.fctr prdl.descr.my.fctr 0.435273386
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log -0.289403116
## D.weight.post.stop.sum D.weight.post.stop.sum -0.224680142
## D.weight.post.stem.sum D.weight.post.stem.sum -0.222642435
## D.weight.sum D.weight.sum -0.222642435
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.217869269
## D.T.work D.T.work -0.201450795
## UniqueID UniqueID -0.183822134
## cellular.fctr cellular.fctr -0.170760996
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 0.162658473
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log -0.162187330
## D.chrs.uppr.n.log D.chrs.uppr.n.log -0.144881328
## D.chrs.n.log D.chrs.n.log -0.144747058
## color.fctr color.fctr 0.144065997
## D.T.great D.T.great -0.143630845
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.137582953
## D.wrds.unq.n.log D.wrds.unq.n.log -0.137582953
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.136808010
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.127676743
## D.wrds.n.log D.wrds.n.log -0.126601927
## condition.fctr condition.fctr 0.110664837
## startprice.dcm1.is9 startprice.dcm1.is9 -0.108520189
## D.P.air D.P.air -0.100753273
## D.wrds.stop.n.log D.wrds.stop.n.log -0.099756626
## D.terms.post.stem.n D.terms.post.stem.n -0.096522527
## D.terms.post.stop.n D.terms.post.stop.n -0.095170933
## D.P.black D.P.black 0.086766195
## startprice.dgt1.is9 startprice.dgt1.is9 0.081895851
## D.T.item D.T.item 0.081696680
## .clusterid .clusterid -0.078260608
## .clusterid.fctr .clusterid.fctr -0.078260608
## startprice.dgt2.is9 startprice.dgt2.is9 0.076977339
## D.T.fulli D.T.fulli -0.068700204
## D.T.excel D.T.excel 0.068583842
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log 0.068513442
## D.T.damag D.T.damag -0.066566054
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -0.065700086
## storage.fctr storage.fctr -0.063619740
## startprice.dcm2.is9 startprice.dcm2.is9 -0.063381896
## D.T.will D.T.will 0.059021676
## D.T.function. D.T.function. -0.058406757
## D.T.new D.T.new 0.052272261
## D.T.like D.T.like 0.049828794
## D.T.cosmet D.T.cosmet 0.048475782
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log 0.047331637
## D.T.crack D.T.crack -0.038806922
## D.T.list D.T.list 0.038361305
## D.dgts.n.log D.dgts.n.log 0.035945994
## D.T.condit D.T.condit 0.035808430
## D.T.no D.T.no -0.035402395
## D.T.origin D.T.origin 0.033856890
## D.T.box D.T.box -0.032314024
## D.T.hous D.T.hous 0.030457274
## D.T.open D.T.open -0.030101292
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log 0.027173174
## D.T.use D.T.use 0.021673307
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.021089653
## D.T.good D.T.good -0.020868951
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.020842504
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log 0.020345331
## D.T.perfect D.T.perfect 0.019681283
## D.P.mini D.P.mini -0.017732674
## D.T.minor D.T.minor 0.015531088
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.014760747
## D.T.scratch D.T.scratch 0.014169828
## .rnorm .rnorm -0.011184193
## D.T.screen D.T.screen 0.009868377
## D.T.X100 D.T.X100 0.006410486
## D.T.tab D.T.tab -0.006360116
## D.T.affect D.T.affect 0.005925489
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log 0.005647696
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.004961597
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.004344899
## carrier.fctr carrier.fctr 0.002621130
## biddable biddable NA
## sold sold NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## D.P.http D.P.http NA
## D.P.white D.P.white NA
## D.P.gold D.P.gold NA
## D.P.spacegray D.P.spacegray NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## startprice 1 0.870529714
## startprice.log10.cut.fctr 1 0.835918829
## prdl.descr.my.fctr 0 0.435273386
## D.chrs.pnct15.n.log 0 0.289403116
## D.weight.post.stop.sum 0 0.224680142
## D.weight.post.stem.sum 0 0.222642435
## D.weight.sum 1 0.222642435
## D.ratio.weight.sum.wrds.n 0 0.217869269
## D.T.work 1 0.201450795
## UniqueID 1 0.183822134
## cellular.fctr 0 0.170760996
## D.ratio.wrds.stop.n.wrds.n 0 0.162658473
## D.chrs.pnct01.n.log 0 0.162187330
## D.chrs.uppr.n.log 0 0.144881328
## D.chrs.n.log 0 0.144747058
## color.fctr 0 0.144065997
## D.T.great 1 0.143630845
## D.terms.post.stem.n.log 1 0.137582953
## D.wrds.unq.n.log 0 0.137582953
## D.terms.post.stop.n.log 0 0.136808010
## D.weight.sum.stem.stop.Ratio 0 0.127676743
## D.wrds.n.log 0 0.126601927
## condition.fctr 0 0.110664837
## startprice.dcm1.is9 0 0.108520189
## D.P.air 1 0.100753273
## D.wrds.stop.n.log 1 0.099756626
## D.terms.post.stem.n 1 0.096522527
## D.terms.post.stop.n 1 0.095170933
## D.P.black 1 0.086766195
## startprice.dgt1.is9 0 0.081895851
## D.T.item 1 0.081696680
## .clusterid 1 0.078260608
## .clusterid.fctr 0 0.078260608
## startprice.dgt2.is9 0 0.076977339
## D.T.fulli 1 0.068700204
## D.T.excel 1 0.068583842
## D.chrs.pnct14.n.log 0 0.068513442
## D.T.damag 1 0.066566054
## D.chrs.pnct11.n.log 0 0.065700086
## storage.fctr 0 0.063619740
## startprice.dcm2.is9 0 0.063381896
## D.T.will 1 0.059021676
## D.T.function. 1 0.058406757
## D.T.new 1 0.052272261
## D.T.like 1 0.049828794
## D.T.cosmet 1 0.048475782
## D.chrs.pnct03.n.log 0 0.047331637
## D.T.crack 1 0.038806922
## D.T.list 1 0.038361305
## D.dgts.n.log 0 0.035945994
## D.T.condit 1 0.035808430
## D.T.no 1 0.035402395
## D.T.origin 1 0.033856890
## D.T.box 1 0.032314024
## D.T.hous 1 0.030457274
## D.T.open 1 0.030101292
## D.chrs.pnct12.n.log 0 0.027173174
## D.T.use 1 0.021673307
## D.chrs.pnct16.n.log 0 0.021089653
## D.T.good 1 0.020868951
## D.chrs.pnct06.n.log 0 0.020842504
## D.chrs.pnct10.n.log 0 0.020345331
## D.T.perfect 1 0.019681283
## D.P.mini 1 0.017732674
## D.T.minor 1 0.015531088
## D.terms.n.stem.stop.Ratio 0 0.014760747
## D.T.scratch 1 0.014169828
## .rnorm 0 0.011184193
## D.T.screen 1 0.009868377
## D.T.X100 1 0.006410486
## D.T.tab 1 0.006360116
## D.T.affect 1 0.005925489
## D.chrs.pnct05.n.log 0 0.005647696
## D.chrs.pnct13.n.log 0 0.004961597
## D.chrs.pnct08.n.log 0 0.004344899
## carrier.fctr 0 0.002621130
## biddable 0 NA
## sold 1 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct07.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct17.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## D.P.http 1 NA
## D.P.white 1 NA
## D.P.gold 1 NA
## D.P.spacegray 1 NA
## startprice.dgt3.is9 0 NAprint(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df, rsp_var=glb_rsp_var,
nzv.freqCut=glb_nzv_freqCut, nzv.uniqueCut=glb_nzv_uniqueCut)))## [1] "cor(D.chrs.n.log, D.chrs.uppr.n.log)=0.9998"
## [1] "cor(startprice.log10, D.chrs.n.log)=-0.1447"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1449"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.n.log as highly correlated with
## D.chrs.uppr.n.log## [1] "cor(D.terms.post.stop.n.log, D.wrds.unq.n.log)=0.9998"
## [1] "cor(startprice.log10, D.terms.post.stop.n.log)=-0.1368"
## [1] "cor(startprice.log10, D.wrds.unq.n.log)=-0.1376"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.post.stop.n.log as highly correlated
## with D.wrds.unq.n.log## [1] "cor(D.weight.post.stem.sum, D.weight.post.stop.sum)=0.9978"
## [1] "cor(startprice.log10, D.weight.post.stem.sum)=-0.2226"
## [1] "cor(startprice.log10, D.weight.post.stop.sum)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stem.sum as highly correlated
## with D.weight.post.stop.sum## [1] "cor(D.chrs.uppr.n.log, D.wrds.n.log)=0.9930"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1449"
## [1] "cor(startprice.log10, D.wrds.n.log)=-0.1266"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.n.log as highly correlated with
## D.chrs.uppr.n.log## [1] "cor(D.chrs.uppr.n.log, D.wrds.unq.n.log)=0.9870"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1449"
## [1] "cor(startprice.log10, D.wrds.unq.n.log)=-0.1376"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.unq.n.log as highly correlated with
## D.chrs.uppr.n.log## [1] "cor(D.chrs.uppr.n.log, D.weight.post.stop.sum)=0.9543"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1449"
## [1] "cor(startprice.log10, D.weight.post.stop.sum)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.uppr.n.log as highly correlated with
## D.weight.post.stop.sum## [1] "cor(D.ratio.wrds.stop.n.wrds.n, D.weight.post.stop.sum)=-0.9291"
## [1] "cor(startprice.log10, D.ratio.wrds.stop.n.wrds.n)=0.1627"
## [1] "cor(startprice.log10, D.weight.post.stop.sum)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.wrds.stop.n.wrds.n as highly correlated
## with D.weight.post.stop.sum## [1] "cor(startprice.dcm1.is9, startprice.dcm2.is9)=0.7621"
## [1] "cor(startprice.log10, startprice.dcm1.is9)=-0.1085"
## [1] "cor(startprice.log10, startprice.dcm2.is9)=-0.0634"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified startprice.dcm2.is9 as highly correlated with
## startprice.dcm1.is9## [1] "cor(D.chrs.pnct06.n.log, D.chrs.pnct16.n.log)=0.7318"
## [1] "cor(startprice.log10, D.chrs.pnct06.n.log)=-0.0208"
## [1] "cor(startprice.log10, D.chrs.pnct16.n.log)=0.0211"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.pnct06.n.log as highly correlated with
## D.chrs.pnct16.n.log## id cor.y
## startprice startprice 0.870529714
## startprice.log10.cut.fctr startprice.log10.cut.fctr 0.835918829
## prdl.descr.my.fctr prdl.descr.my.fctr 0.435273386
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 0.162658473
## color.fctr color.fctr 0.144065997
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.127676743
## condition.fctr condition.fctr 0.110664837
## D.P.black D.P.black 0.086766195
## startprice.dgt1.is9 startprice.dgt1.is9 0.081895851
## D.T.item D.T.item 0.081696680
## startprice.dgt2.is9 startprice.dgt2.is9 0.076977339
## D.T.excel D.T.excel 0.068583842
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log 0.068513442
## D.T.will D.T.will 0.059021676
## D.T.new D.T.new 0.052272261
## D.T.like D.T.like 0.049828794
## D.T.cosmet D.T.cosmet 0.048475782
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log 0.047331637
## D.T.list D.T.list 0.038361305
## D.dgts.n.log D.dgts.n.log 0.035945994
## D.T.condit D.T.condit 0.035808430
## D.T.origin D.T.origin 0.033856890
## D.T.hous D.T.hous 0.030457274
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log 0.027173174
## D.T.use D.T.use 0.021673307
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.021089653
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log 0.020345331
## D.T.perfect D.T.perfect 0.019681283
## D.T.minor D.T.minor 0.015531088
## D.T.scratch D.T.scratch 0.014169828
## D.T.screen D.T.screen 0.009868377
## D.T.X100 D.T.X100 0.006410486
## D.T.affect D.T.affect 0.005925489
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log 0.005647696
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log 0.004961597
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.004344899
## carrier.fctr carrier.fctr 0.002621130
## D.T.tab D.T.tab -0.006360116
## .rnorm .rnorm -0.011184193
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.014760747
## D.P.mini D.P.mini -0.017732674
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.020842504
## D.T.good D.T.good -0.020868951
## D.T.open D.T.open -0.030101292
## D.T.box D.T.box -0.032314024
## D.T.no D.T.no -0.035402395
## D.T.crack D.T.crack -0.038806922
## D.T.function. D.T.function. -0.058406757
## startprice.dcm2.is9 startprice.dcm2.is9 -0.063381896
## storage.fctr storage.fctr -0.063619740
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -0.065700086
## D.T.damag D.T.damag -0.066566054
## D.T.fulli D.T.fulli -0.068700204
## .clusterid .clusterid -0.078260608
## .clusterid.fctr .clusterid.fctr -0.078260608
## D.terms.post.stop.n D.terms.post.stop.n -0.095170933
## D.terms.post.stem.n D.terms.post.stem.n -0.096522527
## D.wrds.stop.n.log D.wrds.stop.n.log -0.099756626
## D.P.air D.P.air -0.100753273
## startprice.dcm1.is9 startprice.dcm1.is9 -0.108520189
## D.wrds.n.log D.wrds.n.log -0.126601927
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.136808010
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.137582953
## D.wrds.unq.n.log D.wrds.unq.n.log -0.137582953
## D.T.great D.T.great -0.143630845
## D.chrs.n.log D.chrs.n.log -0.144747058
## D.chrs.uppr.n.log D.chrs.uppr.n.log -0.144881328
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log -0.162187330
## cellular.fctr cellular.fctr -0.170760996
## UniqueID UniqueID -0.183822134
## D.T.work D.T.work -0.201450795
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.217869269
## D.weight.post.stem.sum D.weight.post.stem.sum -0.222642435
## D.weight.sum D.weight.sum -0.222642435
## D.weight.post.stop.sum D.weight.post.stop.sum -0.224680142
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log -0.289403116
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.P.spacegray D.P.spacegray NA
## D.P.white D.P.white NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## sold sold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## startprice 1 0.870529714
## startprice.log10.cut.fctr 1 0.835918829
## prdl.descr.my.fctr 0 0.435273386
## D.ratio.wrds.stop.n.wrds.n 0 0.162658473
## color.fctr 0 0.144065997
## D.weight.sum.stem.stop.Ratio 0 0.127676743
## condition.fctr 0 0.110664837
## D.P.black 1 0.086766195
## startprice.dgt1.is9 0 0.081895851
## D.T.item 1 0.081696680
## startprice.dgt2.is9 0 0.076977339
## D.T.excel 1 0.068583842
## D.chrs.pnct14.n.log 0 0.068513442
## D.T.will 1 0.059021676
## D.T.new 1 0.052272261
## D.T.like 1 0.049828794
## D.T.cosmet 1 0.048475782
## D.chrs.pnct03.n.log 0 0.047331637
## D.T.list 1 0.038361305
## D.dgts.n.log 0 0.035945994
## D.T.condit 1 0.035808430
## D.T.origin 1 0.033856890
## D.T.hous 1 0.030457274
## D.chrs.pnct12.n.log 0 0.027173174
## D.T.use 1 0.021673307
## D.chrs.pnct16.n.log 0 0.021089653
## D.chrs.pnct10.n.log 0 0.020345331
## D.T.perfect 1 0.019681283
## D.T.minor 1 0.015531088
## D.T.scratch 1 0.014169828
## D.T.screen 1 0.009868377
## D.T.X100 1 0.006410486
## D.T.affect 1 0.005925489
## D.chrs.pnct05.n.log 0 0.005647696
## D.chrs.pnct13.n.log 0 0.004961597
## D.chrs.pnct08.n.log 0 0.004344899
## carrier.fctr 0 0.002621130
## D.T.tab 1 0.006360116
## .rnorm 0 0.011184193
## D.terms.n.stem.stop.Ratio 0 0.014760747
## D.P.mini 1 0.017732674
## D.chrs.pnct06.n.log 0 0.020842504
## D.T.good 1 0.020868951
## D.T.open 1 0.030101292
## D.T.box 1 0.032314024
## D.T.no 1 0.035402395
## D.T.crack 1 0.038806922
## D.T.function. 1 0.058406757
## startprice.dcm2.is9 0 0.063381896
## storage.fctr 0 0.063619740
## D.chrs.pnct11.n.log 0 0.065700086
## D.T.damag 1 0.066566054
## D.T.fulli 1 0.068700204
## .clusterid 1 0.078260608
## .clusterid.fctr 0 0.078260608
## D.terms.post.stop.n 1 0.095170933
## D.terms.post.stem.n 1 0.096522527
## D.wrds.stop.n.log 1 0.099756626
## D.P.air 1 0.100753273
## startprice.dcm1.is9 0 0.108520189
## D.wrds.n.log 0 0.126601927
## D.terms.post.stop.n.log 0 0.136808010
## D.terms.post.stem.n.log 1 0.137582953
## D.wrds.unq.n.log 0 0.137582953
## D.T.great 1 0.143630845
## D.chrs.n.log 0 0.144747058
## D.chrs.uppr.n.log 0 0.144881328
## D.chrs.pnct01.n.log 0 0.162187330
## cellular.fctr 0 0.170760996
## UniqueID 1 0.183822134
## D.T.work 1 0.201450795
## D.ratio.weight.sum.wrds.n 0 0.217869269
## D.weight.post.stem.sum 0 0.222642435
## D.weight.sum 1 0.222642435
## D.weight.post.stop.sum 0 0.224680142
## D.chrs.pnct15.n.log 0 0.289403116
## D.P.gold 1 NA
## D.P.http 1 NA
## D.P.spacegray 1 NA
## D.P.white 1 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct07.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct17.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## biddable 0 NA
## sold 1 NA
## startprice.dgt3.is9 0 NA
## cor.high.X freqRatio
## startprice <NA> 1.000000
## startprice.log10.cut.fctr <NA> 3.280000
## prdl.descr.my.fctr <NA> 1.052632
## D.ratio.wrds.stop.n.wrds.n D.weight.post.stop.sum 11.090909
## color.fctr <NA> 1.578947
## D.weight.sum.stem.stop.Ratio <NA> 51.000000
## condition.fctr <NA> 4.125000
## D.P.black <NA> 216.000000
## startprice.dgt1.is9 <NA> 1.646341
## D.T.item <NA> 52.250000
## startprice.dgt2.is9 <NA> 5.781250
## D.T.excel <NA> 69.666667
## D.chrs.pnct14.n.log <NA> 42.200000
## D.T.will <NA> 53.000000
## D.T.new <NA> 103.500000
## D.T.like <NA> 213.000000
## D.T.cosmet <NA> 34.500000
## D.chrs.pnct03.n.log <NA> 216.000000
## D.T.list <NA> 216.000000
## D.dgts.n.log <NA> 19.700000
## D.T.condit <NA> 14.416667
## D.T.origin <NA> 215.000000
## D.T.hous <NA> 215.000000
## D.chrs.pnct12.n.log <NA> 42.200000
## D.T.use <NA> 32.000000
## D.chrs.pnct16.n.log <NA> 53.000000
## D.chrs.pnct10.n.log <NA> 107.500000
## D.T.perfect <NA> 106.000000
## D.T.minor <NA> 68.333333
## D.T.scratch <NA> 37.400000
## D.T.screen <NA> 68.000000
## D.T.X100 <NA> 215.000000
## D.T.affect <NA> 216.000000
## D.chrs.pnct05.n.log <NA> 107.500000
## D.chrs.pnct13.n.log <NA> 4.735294
## D.chrs.pnct08.n.log <NA> 71.000000
## carrier.fctr <NA> 3.416667
## D.T.tab <NA> 216.000000
## .rnorm <NA> 1.000000
## D.terms.n.stem.stop.Ratio <NA> 70.666667
## D.P.mini <NA> 215.000000
## D.chrs.pnct06.n.log D.chrs.pnct16.n.log 215.000000
## D.T.good <NA> 24.500000
## D.T.open <NA> 214.000000
## D.T.box <NA> 213.000000
## D.T.no <NA> 69.000000
## D.T.crack <NA> 105.000000
## D.T.function. <NA> 213.000000
## startprice.dcm2.is9 startprice.dcm1.is9 2.677966
## storage.fctr <NA> 2.707317
## D.chrs.pnct11.n.log <NA> 14.846154
## D.T.damag <NA> 215.000000
## D.T.fulli <NA> 215.000000
## .clusterid <NA> 9.421053
## .clusterid.fctr <NA> 9.421053
## D.terms.post.stop.n <NA> 8.133333
## D.terms.post.stem.n <NA> 8.133333
## D.wrds.stop.n.log <NA> 11.166667
## D.P.air <NA> 216.000000
## startprice.dcm1.is9 <NA> 1.932432
## D.wrds.n.log D.chrs.uppr.n.log 12.200000
## D.terms.post.stop.n.log D.wrds.unq.n.log 8.133333
## D.terms.post.stem.n.log <NA> 8.133333
## D.wrds.unq.n.log D.chrs.uppr.n.log 8.133333
## D.T.great <NA> 103.000000
## D.chrs.n.log D.chrs.uppr.n.log 15.250000
## D.chrs.uppr.n.log D.weight.post.stop.sum 12.200000
## D.chrs.pnct01.n.log <NA> 106.500000
## cellular.fctr <NA> 1.892308
## UniqueID <NA> 1.000000
## D.T.work <NA> 51.250000
## D.ratio.weight.sum.wrds.n <NA> 30.500000
## D.weight.post.stem.sum D.weight.post.stop.sum 30.500000
## D.weight.sum <NA> 30.500000
## D.weight.post.stop.sum <NA> 30.500000
## D.chrs.pnct15.n.log <NA> 41.600000
## D.P.gold <NA> 0.000000
## D.P.http <NA> 0.000000
## D.P.spacegray <NA> 0.000000
## D.P.white <NA> 0.000000
## D.chrs.pnct02.n.log <NA> 0.000000
## D.chrs.pnct04.n.log <NA> 0.000000
## D.chrs.pnct07.n.log <NA> 0.000000
## D.chrs.pnct09.n.log <NA> 0.000000
## D.chrs.pnct17.n.log <NA> 0.000000
## D.chrs.pnct18.n.log <NA> 0.000000
## D.chrs.pnct19.n.log <NA> 0.000000
## D.chrs.pnct20.n.log <NA> 0.000000
## D.chrs.pnct21.n.log <NA> 0.000000
## D.chrs.pnct22.n.log <NA> 0.000000
## D.chrs.pnct23.n.log <NA> 0.000000
## D.chrs.pnct24.n.log <NA> 0.000000
## D.chrs.pnct25.n.log <NA> 0.000000
## D.chrs.pnct26.n.log <NA> 0.000000
## D.chrs.pnct27.n.log <NA> 0.000000
## D.chrs.pnct28.n.log <NA> 0.000000
## D.chrs.pnct29.n.log <NA> 0.000000
## D.chrs.pnct30.n.log <NA> 0.000000
## biddable <NA> 0.000000
## sold <NA> 0.000000
## startprice.dgt3.is9 <NA> 0.000000
## percentUnique zeroVar nzv is.cor.y.abs.low
## startprice 64.5161290 FALSE FALSE FALSE
## startprice.log10.cut.fctr 1.3824885 FALSE FALSE FALSE
## prdl.descr.my.fctr 9.2165899 FALSE FALSE FALSE
## D.ratio.wrds.stop.n.wrds.n 19.3548387 FALSE FALSE FALSE
## color.fctr 2.3041475 FALSE FALSE FALSE
## D.weight.sum.stem.stop.Ratio 27.1889401 FALSE FALSE FALSE
## condition.fctr 2.7649770 FALSE FALSE FALSE
## D.P.black 0.9216590 FALSE TRUE FALSE
## startprice.dgt1.is9 0.9216590 FALSE FALSE FALSE
## D.T.item 2.7649770 FALSE TRUE FALSE
## startprice.dgt2.is9 0.9216590 FALSE FALSE FALSE
## D.T.excel 3.2258065 FALSE TRUE FALSE
## D.chrs.pnct14.n.log 1.3824885 FALSE TRUE FALSE
## D.T.will 1.3824885 FALSE TRUE FALSE
## D.T.new 3.6866359 FALSE TRUE FALSE
## D.T.like 2.3041475 FALSE TRUE FALSE
## D.T.cosmet 1.8433180 FALSE TRUE FALSE
## D.chrs.pnct03.n.log 0.9216590 FALSE TRUE FALSE
## D.T.list 0.9216590 FALSE TRUE FALSE
## D.dgts.n.log 2.7649770 FALSE TRUE FALSE
## D.T.condit 5.9907834 FALSE FALSE FALSE
## D.T.origin 1.3824885 FALSE TRUE FALSE
## D.T.hous 1.3824885 FALSE TRUE FALSE
## D.chrs.pnct12.n.log 1.3824885 FALSE TRUE FALSE
## D.T.use 5.9907834 FALSE FALSE FALSE
## D.chrs.pnct16.n.log 1.3824885 FALSE TRUE FALSE
## D.chrs.pnct10.n.log 0.9216590 FALSE TRUE FALSE
## D.T.perfect 2.3041475 FALSE TRUE FALSE
## D.T.minor 3.2258065 FALSE TRUE FALSE
## D.T.scratch 5.5299539 FALSE FALSE FALSE
## D.T.screen 4.6082949 FALSE FALSE TRUE
## D.T.X100 1.3824885 FALSE TRUE TRUE
## D.T.affect 0.9216590 FALSE TRUE TRUE
## D.chrs.pnct05.n.log 0.9216590 FALSE TRUE TRUE
## D.chrs.pnct13.n.log 1.8433180 FALSE FALSE TRUE
## D.chrs.pnct08.n.log 1.3824885 FALSE TRUE TRUE
## carrier.fctr 2.7649770 FALSE FALSE TRUE
## D.T.tab 0.9216590 FALSE TRUE TRUE
## .rnorm 100.0000000 FALSE FALSE FALSE
## D.terms.n.stem.stop.Ratio 1.3824885 FALSE TRUE FALSE
## D.P.mini 1.3824885 FALSE TRUE FALSE
## D.chrs.pnct06.n.log 1.3824885 FALSE TRUE FALSE
## D.T.good 3.2258065 FALSE TRUE FALSE
## D.T.open 1.8433180 FALSE TRUE FALSE
## D.T.box 2.3041475 FALSE TRUE FALSE
## D.T.no 3.2258065 FALSE TRUE FALSE
## D.T.crack 2.7649770 FALSE TRUE FALSE
## D.T.function. 2.3041475 FALSE TRUE FALSE
## startprice.dcm2.is9 0.9216590 FALSE FALSE FALSE
## storage.fctr 2.3041475 FALSE FALSE FALSE
## D.chrs.pnct11.n.log 2.3041475 FALSE FALSE FALSE
## D.T.damag 1.3824885 FALSE TRUE FALSE
## D.T.fulli 1.3824885 FALSE TRUE FALSE
## .clusterid 1.8433180 FALSE FALSE FALSE
## .clusterid.fctr 1.8433180 FALSE FALSE FALSE
## D.terms.post.stop.n 5.9907834 FALSE FALSE FALSE
## D.terms.post.stem.n 5.9907834 FALSE FALSE FALSE
## D.wrds.stop.n.log 6.9124424 FALSE FALSE FALSE
## D.P.air 0.9216590 FALSE TRUE FALSE
## startprice.dcm1.is9 0.9216590 FALSE FALSE FALSE
## D.wrds.n.log 9.2165899 FALSE FALSE FALSE
## D.terms.post.stop.n.log 5.9907834 FALSE FALSE FALSE
## D.terms.post.stem.n.log 5.9907834 FALSE FALSE FALSE
## D.wrds.unq.n.log 5.9907834 FALSE FALSE FALSE
## D.T.great 3.6866359 FALSE TRUE FALSE
## D.chrs.n.log 21.6589862 FALSE FALSE FALSE
## D.chrs.uppr.n.log 21.1981567 FALSE FALSE FALSE
## D.chrs.pnct01.n.log 1.8433180 FALSE TRUE FALSE
## cellular.fctr 1.3824885 FALSE FALSE FALSE
## UniqueID 100.0000000 FALSE FALSE FALSE
## D.T.work 3.6866359 FALSE TRUE FALSE
## D.ratio.weight.sum.wrds.n 39.1705069 FALSE FALSE FALSE
## D.weight.post.stem.sum 38.7096774 FALSE FALSE FALSE
## D.weight.sum 38.7096774 FALSE FALSE FALSE
## D.weight.post.stop.sum 38.7096774 FALSE FALSE FALSE
## D.chrs.pnct15.n.log 1.3824885 FALSE TRUE FALSE
## D.P.gold 0.4608295 TRUE TRUE NA
## D.P.http 0.4608295 TRUE TRUE NA
## D.P.spacegray 0.4608295 TRUE TRUE NA
## D.P.white 0.4608295 TRUE TRUE NA
## D.chrs.pnct02.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct04.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct07.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct09.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct17.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct18.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct19.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct20.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct21.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct22.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct23.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct24.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct25.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct26.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct27.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct28.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct29.n.log 0.4608295 TRUE TRUE NA
## D.chrs.pnct30.n.log 0.4608295 TRUE TRUE NA
## biddable 0.4608295 TRUE TRUE NA
## sold 0.4608295 TRUE TRUE NA
## startprice.dgt3.is9 0.4608295 TRUE TRUE NAplt_feats_df <- glb_feats_df
print(myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
colorcol_name="nzv", jitter=TRUE) +
#geom_point(aes(shape=nzv)) +
geom_point() +
xlim(-5, 25) +
geom_hline(yintercept=glb_nzv_freqCut) +
geom_vline(xintercept=glb_nzv_uniqueCut))## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_point).
print(subset(glb_feats_df, nzv))## id cor.y
## D.P.black D.P.black 0.086766195
## D.T.item D.T.item 0.081696680
## D.T.excel D.T.excel 0.068583842
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log 0.068513442
## D.T.will D.T.will 0.059021676
## D.T.new D.T.new 0.052272261
## D.T.like D.T.like 0.049828794
## D.T.cosmet D.T.cosmet 0.048475782
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log 0.047331637
## D.T.list D.T.list 0.038361305
## D.dgts.n.log D.dgts.n.log 0.035945994
## D.T.origin D.T.origin 0.033856890
## D.T.hous D.T.hous 0.030457274
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log 0.027173174
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.021089653
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log 0.020345331
## D.T.perfect D.T.perfect 0.019681283
## D.T.minor D.T.minor 0.015531088
## D.T.X100 D.T.X100 0.006410486
## D.T.affect D.T.affect 0.005925489
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log 0.005647696
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 0.004344899
## D.T.tab D.T.tab -0.006360116
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio -0.014760747
## D.P.mini D.P.mini -0.017732674
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.020842504
## D.T.good D.T.good -0.020868951
## D.T.open D.T.open -0.030101292
## D.T.box D.T.box -0.032314024
## D.T.no D.T.no -0.035402395
## D.T.crack D.T.crack -0.038806922
## D.T.function. D.T.function. -0.058406757
## D.T.damag D.T.damag -0.066566054
## D.T.fulli D.T.fulli -0.068700204
## D.P.air D.P.air -0.100753273
## D.T.great D.T.great -0.143630845
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log -0.162187330
## D.T.work D.T.work -0.201450795
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log -0.289403116
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.P.spacegray D.P.spacegray NA
## D.P.white D.P.white NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## sold sold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs cor.high.X
## D.P.black 1 0.086766195 <NA>
## D.T.item 1 0.081696680 <NA>
## D.T.excel 1 0.068583842 <NA>
## D.chrs.pnct14.n.log 0 0.068513442 <NA>
## D.T.will 1 0.059021676 <NA>
## D.T.new 1 0.052272261 <NA>
## D.T.like 1 0.049828794 <NA>
## D.T.cosmet 1 0.048475782 <NA>
## D.chrs.pnct03.n.log 0 0.047331637 <NA>
## D.T.list 1 0.038361305 <NA>
## D.dgts.n.log 0 0.035945994 <NA>
## D.T.origin 1 0.033856890 <NA>
## D.T.hous 1 0.030457274 <NA>
## D.chrs.pnct12.n.log 0 0.027173174 <NA>
## D.chrs.pnct16.n.log 0 0.021089653 <NA>
## D.chrs.pnct10.n.log 0 0.020345331 <NA>
## D.T.perfect 1 0.019681283 <NA>
## D.T.minor 1 0.015531088 <NA>
## D.T.X100 1 0.006410486 <NA>
## D.T.affect 1 0.005925489 <NA>
## D.chrs.pnct05.n.log 0 0.005647696 <NA>
## D.chrs.pnct08.n.log 0 0.004344899 <NA>
## D.T.tab 1 0.006360116 <NA>
## D.terms.n.stem.stop.Ratio 0 0.014760747 <NA>
## D.P.mini 1 0.017732674 <NA>
## D.chrs.pnct06.n.log 0 0.020842504 D.chrs.pnct16.n.log
## D.T.good 1 0.020868951 <NA>
## D.T.open 1 0.030101292 <NA>
## D.T.box 1 0.032314024 <NA>
## D.T.no 1 0.035402395 <NA>
## D.T.crack 1 0.038806922 <NA>
## D.T.function. 1 0.058406757 <NA>
## D.T.damag 1 0.066566054 <NA>
## D.T.fulli 1 0.068700204 <NA>
## D.P.air 1 0.100753273 <NA>
## D.T.great 1 0.143630845 <NA>
## D.chrs.pnct01.n.log 0 0.162187330 <NA>
## D.T.work 1 0.201450795 <NA>
## D.chrs.pnct15.n.log 0 0.289403116 <NA>
## D.P.gold 1 NA <NA>
## D.P.http 1 NA <NA>
## D.P.spacegray 1 NA <NA>
## D.P.white 1 NA <NA>
## D.chrs.pnct02.n.log 0 NA <NA>
## D.chrs.pnct04.n.log 0 NA <NA>
## D.chrs.pnct07.n.log 0 NA <NA>
## D.chrs.pnct09.n.log 0 NA <NA>
## D.chrs.pnct17.n.log 0 NA <NA>
## D.chrs.pnct18.n.log 0 NA <NA>
## D.chrs.pnct19.n.log 0 NA <NA>
## D.chrs.pnct20.n.log 0 NA <NA>
## D.chrs.pnct21.n.log 0 NA <NA>
## D.chrs.pnct22.n.log 0 NA <NA>
## D.chrs.pnct23.n.log 0 NA <NA>
## D.chrs.pnct24.n.log 0 NA <NA>
## D.chrs.pnct25.n.log 0 NA <NA>
## D.chrs.pnct26.n.log 0 NA <NA>
## D.chrs.pnct27.n.log 0 NA <NA>
## D.chrs.pnct28.n.log 0 NA <NA>
## D.chrs.pnct29.n.log 0 NA <NA>
## D.chrs.pnct30.n.log 0 NA <NA>
## biddable 0 NA <NA>
## sold 1 NA <NA>
## startprice.dgt3.is9 0 NA <NA>
## freqRatio percentUnique zeroVar nzv
## D.P.black 216.00000 0.9216590 FALSE TRUE
## D.T.item 52.25000 2.7649770 FALSE TRUE
## D.T.excel 69.66667 3.2258065 FALSE TRUE
## D.chrs.pnct14.n.log 42.20000 1.3824885 FALSE TRUE
## D.T.will 53.00000 1.3824885 FALSE TRUE
## D.T.new 103.50000 3.6866359 FALSE TRUE
## D.T.like 213.00000 2.3041475 FALSE TRUE
## D.T.cosmet 34.50000 1.8433180 FALSE TRUE
## D.chrs.pnct03.n.log 216.00000 0.9216590 FALSE TRUE
## D.T.list 216.00000 0.9216590 FALSE TRUE
## D.dgts.n.log 19.70000 2.7649770 FALSE TRUE
## D.T.origin 215.00000 1.3824885 FALSE TRUE
## D.T.hous 215.00000 1.3824885 FALSE TRUE
## D.chrs.pnct12.n.log 42.20000 1.3824885 FALSE TRUE
## D.chrs.pnct16.n.log 53.00000 1.3824885 FALSE TRUE
## D.chrs.pnct10.n.log 107.50000 0.9216590 FALSE TRUE
## D.T.perfect 106.00000 2.3041475 FALSE TRUE
## D.T.minor 68.33333 3.2258065 FALSE TRUE
## D.T.X100 215.00000 1.3824885 FALSE TRUE
## D.T.affect 216.00000 0.9216590 FALSE TRUE
## D.chrs.pnct05.n.log 107.50000 0.9216590 FALSE TRUE
## D.chrs.pnct08.n.log 71.00000 1.3824885 FALSE TRUE
## D.T.tab 216.00000 0.9216590 FALSE TRUE
## D.terms.n.stem.stop.Ratio 70.66667 1.3824885 FALSE TRUE
## D.P.mini 215.00000 1.3824885 FALSE TRUE
## D.chrs.pnct06.n.log 215.00000 1.3824885 FALSE TRUE
## D.T.good 24.50000 3.2258065 FALSE TRUE
## D.T.open 214.00000 1.8433180 FALSE TRUE
## D.T.box 213.00000 2.3041475 FALSE TRUE
## D.T.no 69.00000 3.2258065 FALSE TRUE
## D.T.crack 105.00000 2.7649770 FALSE TRUE
## D.T.function. 213.00000 2.3041475 FALSE TRUE
## D.T.damag 215.00000 1.3824885 FALSE TRUE
## D.T.fulli 215.00000 1.3824885 FALSE TRUE
## D.P.air 216.00000 0.9216590 FALSE TRUE
## D.T.great 103.00000 3.6866359 FALSE TRUE
## D.chrs.pnct01.n.log 106.50000 1.8433180 FALSE TRUE
## D.T.work 51.25000 3.6866359 FALSE TRUE
## D.chrs.pnct15.n.log 41.60000 1.3824885 FALSE TRUE
## D.P.gold 0.00000 0.4608295 TRUE TRUE
## D.P.http 0.00000 0.4608295 TRUE TRUE
## D.P.spacegray 0.00000 0.4608295 TRUE TRUE
## D.P.white 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct02.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct04.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct07.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct09.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct17.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct18.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct19.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct20.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct21.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct22.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct23.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct24.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct25.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct26.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct27.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct28.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct29.n.log 0.00000 0.4608295 TRUE TRUE
## D.chrs.pnct30.n.log 0.00000 0.4608295 TRUE TRUE
## biddable 0.00000 0.4608295 TRUE TRUE
## sold 0.00000 0.4608295 TRUE TRUE
## startprice.dgt3.is9 0.00000 0.4608295 TRUE TRUE
## is.cor.y.abs.low
## D.P.black FALSE
## D.T.item FALSE
## D.T.excel FALSE
## D.chrs.pnct14.n.log FALSE
## D.T.will FALSE
## D.T.new FALSE
## D.T.like FALSE
## D.T.cosmet FALSE
## D.chrs.pnct03.n.log FALSE
## D.T.list FALSE
## D.dgts.n.log FALSE
## D.T.origin FALSE
## D.T.hous FALSE
## D.chrs.pnct12.n.log FALSE
## D.chrs.pnct16.n.log FALSE
## D.chrs.pnct10.n.log FALSE
## D.T.perfect FALSE
## D.T.minor FALSE
## D.T.X100 TRUE
## D.T.affect TRUE
## D.chrs.pnct05.n.log TRUE
## D.chrs.pnct08.n.log TRUE
## D.T.tab TRUE
## D.terms.n.stem.stop.Ratio FALSE
## D.P.mini FALSE
## D.chrs.pnct06.n.log FALSE
## D.T.good FALSE
## D.T.open FALSE
## D.T.box FALSE
## D.T.no FALSE
## D.T.crack FALSE
## D.T.function. FALSE
## D.T.damag FALSE
## D.T.fulli FALSE
## D.P.air FALSE
## D.T.great FALSE
## D.chrs.pnct01.n.log FALSE
## D.T.work FALSE
## D.chrs.pnct15.n.log FALSE
## D.P.gold NA
## D.P.http NA
## D.P.spacegray NA
## D.P.white NA
## D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log NA
## D.chrs.pnct07.n.log NA
## D.chrs.pnct09.n.log NA
## D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log NA
## biddable NA
## sold NA
## startprice.dgt3.is9 NAtmp_allobs_df <-
glb_allobs_df[, union(setdiff(names(glb_allobs_df), subset(glb_feats_df, nzv)$id),
glb_cluster_entropy_var)]
glb_trnobs_df <- subset(tmp_allobs_df, .src == "Train")
glb_newobs_df <- subset(tmp_allobs_df, .src == "Test")
glb_feats_df$interaction.feat <- NA
for (feat in names(glb_interaction_only_feats_lst))
glb_feats_df[glb_feats_df$id %in% feat, "interaction.feat"] <-
glb_interaction_only_feats_lst[[feat]]
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
numeric_indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
glb_feats_df$shapiro.test.p.value <- NA
glb_feats_df[glb_feats_df$id %in% numeric_indep_vars, "shapiro.test.p.value"] <-
sapply(numeric_indep_vars, function(var) shapiro.test(glb_trnobs_df[, var])$p.value)
not_nrml_feats_df <- glb_feats_df %>%
subset(!is.na(shapiro.test.p.value)) %>%
subset((shapiro.test.p.value < 0.05) || (id == ".rnorm")) %>%
arrange(shapiro.test.p.value)
row.names(not_nrml_feats_df) <- not_nrml_feats_df$id
#plt_trnobs_df <- glb_trnobs_df[, c("D.npnct05.log", ".rnorm")]
plt_trnobs_df <- glb_trnobs_df[, c(union(not_nrml_feats_df$id[1:min(5, nrow(not_nrml_feats_df))],
".rnorm"), glb_cluster_entropy_var)]
print(myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df), glb_cluster_entropy_var),
xcol_name = glb_cluster_entropy_var) +
facet_wrap(~variable, scales="free"))## Warning in myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df),
## glb_cluster_entropy_var), : xcol_name:sold is not a factor; creating
## sold_fctr
#myplot_histogram(plt_trnobs_df, "D.npnct11.log", fill_col_name="sold", show_stats = TRUE)
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
# shd .clusterid.fctr be excluded from this ? or include encoding of glb_category_var:.clusterid.fctr ?
indep_vars <-
myadjust_interaction_feats(subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"])
myrun_rfe <- function(obs_df, indep_vars, sizes=NULL) {
rfe_obs_df <- myget_vectorized_obs_df(obs_df, glb_rsp_var, indep_vars)
predictors_vctr <- setdiff(names(rfe_obs_df), glb_rsp_var)
if (is.null(sizes))
sizes <- tail(2^(1:as.integer(log2(length(predictors_vctr)))), 5)
rfe_control <- rfeControl(functions=rfFuncs, method="repeatedcv", number=glb_rcv_n_folds,
repeats=glb_rcv_n_repeats, verbose=TRUE, returnResamp = "all",
seeds=mygen_seeds(seeds_lst_len=(glb_rcv_n_folds * glb_rcv_n_repeats) + 1,
seeds_elmnt_lst_len=(length(sizes) + 1)))
set.seed(113)
rfe_results <- rfe(rfe_obs_df[, predictors_vctr],
rfe_obs_df[, glb_rsp_var],
sizes=sizes, metric=unlist(strsplit(glb_model_evl_criteria, "[.]"))[2],
maximize=ifelse(unlist(strsplit(glb_model_evl_criteria, "[.]"))[1] == "max",
TRUE, FALSE),
rfeControl=rfe_control)
print(rfe_results)
print(predictors(rfe_results))
# print(plot(rfe_results, type=c("g", "o")))
# print(plot(rfe_results))
print(ggplot(rfe_results))
return(rfe_results)
}
rfe_fit_results <- myrun_rfe(glb_fitobs_df, indep_vars, glb_rfe_fit_sizes)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables RMSE Rsquared RMSESD RsquaredSD Selected
## 106 0.1463 0.7400 0.009329 0.04095
## 111 0.1461 0.7406 0.008715 0.04117 *
## 116 0.1463 0.7397 0.008626 0.04010
## 120 0.1470 0.7382 0.009002 0.04190
## 128 0.1468 0.7382 0.010435 0.04572
## 147 0.1484 0.7339 0.009747 0.05085
##
## The top 5 variables (out of 111):
## prdl.descr.my.fctriPad1#0, condition.fctrFor parts or not working, condition.fctrNew, prdl.descr.my.fctriPadAir2#0, prdl.descr.my.fctriPad1#1
##
## [1] "prdl.descr.my.fctriPad1#0"
## [2] "condition.fctrFor parts or not working"
## [3] "condition.fctrNew"
## [4] "prdl.descr.my.fctriPadAir2#0"
## [5] "prdl.descr.my.fctriPad1#1"
## [6] "storage.fctr16"
## [7] "prdl.descr.my.fctriPadAir#0"
## [8] "D.weight.post.stem.sum"
## [9] "prdl.descr.my.fctriPad2#0"
## [10] "D.weight.post.stop.sum"
## [11] "storage.fctr64"
## [12] "prdl.descr.my.fctriPadmini3#0"
## [13] "prdl.descr.my.fctriPad2#1"
## [14] "prdl.descr.my.fctriPadmini#0"
## [15] "cellular.fctr0:carrier.fctrNone"
## [16] "prdl.descr.my.fctriPadAir2#1"
## [17] "color.fctrGold"
## [18] "prdl.descr.my.fctriPadmini2#0"
## [19] "prdl.descr.my.fctriPad4#0"
## [20] "storage.fctr32"
## [21] "D.chrs.uppr.n.log"
## [22] "prdl.descr.my.fctriPadAir#1"
## [23] "storage.fctrUnknown"
## [24] "prdl.descr.my.fctriPadmini#1"
## [25] "cellular.fctrUnknown:carrier.fctrUnknown"
## [26] "D.terms.post.stop.n.log"
## [27] "color.fctrWhite"
## [28] "D.ratio.weight.sum.wrds.n"
## [29] "D.ratio.wrds.stop.n.wrds.n"
## [30] "D.wrds.unq.n.log"
## [31] "D.chrs.n.log"
## [32] "D.weight.sum.stem.stop.Ratio"
## [33] "cellular.fctr1:carrier.fctrT-Mobile"
## [34] "D.wrds.n.log"
## [35] "prdl.descr.my.fctrUnknown#1"
## [36] "cellular.fctrUnknown"
## [37] "prdl.descr.my.fctriPad3#1"
## [38] "cellular.fctr1"
## [39] "startprice.dcm1.is9"
## [40] "startprice.dgt2.is9"
## [41] "condition.fctrManufacturer refurbished"
## [42] "color.fctrSpace Gray"
## [43] "prdl.descr.my.fctrUnknown#0"
## [44] "condition.fctrNew other (see details)"
## [45] "prdl.descr.my.fctriPad4#1"
## [46] "D.chrs.pnct13.n.log"
## [47] "prdl.descr.my.fctriPad4#1:.clusterid.fctr3"
## [48] "cellular.fctr1:carrier.fctrUnknown"
## [49] "prdl.descr.my.fctriPad1#1:.clusterid.fctr4"
## [50] "prdl.descr.my.fctriPad2#1:.clusterid.fctr2"
## [51] "D.chrs.pnct11.n.log"
## [52] "startprice.dcm2.is9"
## [53] "prdl.descr.my.fctriPadmini#1:.clusterid.fctr3"
## [54] "cellular.fctr1:carrier.fctrVerizon"
## [55] "prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2"
## [56] "prdl.descr.my.fctriPad3#0"
## [57] ".rnorm"
## [58] "prdl.descr.my.fctriPad3#1:.clusterid.fctr2"
## [59] "prdl.descr.my.fctriPad4#1:.clusterid.fctr2"
## [60] "cellular.fctr1:carrier.fctrSprint"
## [61] "prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2"
## [62] "prdl.descr.my.fctriPad3#1:.clusterid.fctr3"
## [63] "prdl.descr.my.fctriPadmini2#1"
## [64] "cellular.fctr0:carrier.fctrSprint"
## [65] "cellular.fctr0:carrier.fctrT-Mobile"
## [66] "cellular.fctr0:carrier.fctrUnknown"
## [67] "cellular.fctr0:carrier.fctrVerizon"
## [68] "cellular.fctr1:carrier.fctrNone"
## [69] "cellular.fctrUnknown:carrier.fctrNone"
## [70] "cellular.fctrUnknown:carrier.fctrSprint"
## [71] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [72] "cellular.fctrUnknown:carrier.fctrVerizon"
## [73] "prdl.descr.my.fctrUnknown#0:.clusterid.fctr2"
## [74] "prdl.descr.my.fctrUnknown#0:.clusterid.fctr3"
## [75] "prdl.descr.my.fctrUnknown#0:.clusterid.fctr4"
## [76] "prdl.descr.my.fctrUnknown#0:.clusterid.fctr5"
## [77] "prdl.descr.my.fctrUnknown#1:.clusterid.fctr3"
## [78] "prdl.descr.my.fctrUnknown#1:.clusterid.fctr4"
## [79] "prdl.descr.my.fctrUnknown#1:.clusterid.fctr5"
## [80] "prdl.descr.my.fctriPad1#0:.clusterid.fctr2"
## [81] "prdl.descr.my.fctriPad1#0:.clusterid.fctr3"
## [82] "prdl.descr.my.fctriPad1#0:.clusterid.fctr4"
## [83] "prdl.descr.my.fctriPad1#0:.clusterid.fctr5"
## [84] "prdl.descr.my.fctriPad1#1:.clusterid.fctr2"
## [85] "prdl.descr.my.fctriPad1#1:.clusterid.fctr3"
## [86] "prdl.descr.my.fctriPad1#1:.clusterid.fctr5"
## [87] "prdl.descr.my.fctriPad2#0:.clusterid.fctr2"
## [88] "prdl.descr.my.fctriPad2#0:.clusterid.fctr3"
## [89] "prdl.descr.my.fctriPad2#0:.clusterid.fctr4"
## [90] "prdl.descr.my.fctriPad2#0:.clusterid.fctr5"
## [91] "prdl.descr.my.fctriPad2#1:.clusterid.fctr4"
## [92] "prdl.descr.my.fctriPad2#1:.clusterid.fctr5"
## [93] "prdl.descr.my.fctriPad3#0:.clusterid.fctr2"
## [94] "prdl.descr.my.fctriPad3#0:.clusterid.fctr3"
## [95] "prdl.descr.my.fctriPad3#0:.clusterid.fctr4"
## [96] "prdl.descr.my.fctriPad3#0:.clusterid.fctr5"
## [97] "prdl.descr.my.fctriPad3#1:.clusterid.fctr4"
## [98] "prdl.descr.my.fctriPad3#1:.clusterid.fctr5"
## [99] "prdl.descr.my.fctriPad4#0:.clusterid.fctr2"
## [100] "prdl.descr.my.fctriPad4#0:.clusterid.fctr3"
## [101] "prdl.descr.my.fctriPad4#0:.clusterid.fctr4"
## [102] "prdl.descr.my.fctriPad4#0:.clusterid.fctr5"
## [103] "prdl.descr.my.fctriPad4#1:.clusterid.fctr4"
## [104] "prdl.descr.my.fctriPad4#1:.clusterid.fctr5"
## [105] "prdl.descr.my.fctriPadAir#0:.clusterid.fctr2"
## [106] "prdl.descr.my.fctriPadAir#0:.clusterid.fctr3"
## [107] "prdl.descr.my.fctriPadAir#0:.clusterid.fctr4"
## [108] "prdl.descr.my.fctriPadAir#0:.clusterid.fctr5"
## [109] "prdl.descr.my.fctriPadAir#1:.clusterid.fctr2"
## [110] "prdl.descr.my.fctriPadAir#1:.clusterid.fctr3"
## [111] "prdl.descr.my.fctriPadAir#1:.clusterid.fctr4"
# print(all.equal(rfe_results[-which(names(rfe_results) == "times")],
# sav_rfe_results[-which(names(sav_rfe_results) == "times")]))
# require(mRMRe)
# indep_vars_vctr <- subset(glb_feats_df, !nzv &
# (exclude.as.feat != 1))[, "id"]
# indep_vars_vctr <- setdiff(indep_vars_vctr,
# myfind_fctr_cols_df(glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]))
# tmp_trnobs_df <- glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]
# tmp_trnobs_df$biddable <- as.numeric(tmp_trnobs_df$biddable)
# dd <- mRMR.data(data = tmp_trnobs_df)
# mRMRe.fltr <- mRMR.classic(data = dd, target_indices = c(1), feature_count = 10)
# print(solutions(mRMRe.fltr)[[1]])
# print(apply(solutions(mRMRe.fltr)[[1]], 2, function(x, y) { return(y[x]) },
# y=featureNames(dd)))
# print(featureNames(dd)[solutions(mRMRe.fltr)[[1]]])
# print(mRMRe.fltr@filters); print(mRMRe.fltr@scores)
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 422
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 798
## cellular.fctr D.terms.post.stop.n
## 801 767
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 767 767
## D.terms.post.stem.n D.terms.post.stem.n.log
## 767 767
## D.weight.post.stem.sum D.T.condit
## 767 1102
## D.T.use D.T.new
## 1268 1337
## D.T.scratch D.T.good
## 1257 1307
## D.T.work D.T.screen
## 1313 1310
## D.T.great D.T.box
## 1366 1365
## D.T.no D.T.excel
## 1354 1384
## D.T.like D.T.item
## 1388 1352
## D.T.cosmet D.T.minor
## 1345 1351
## D.T.crack D.T.perfect
## 1397 1401
## D.T.fulli D.T.function.
## 1385 1377
## D.T.hous D.T.origin
## 1366 1391
## D.T.will D.T.X100
## 1406 1379
## D.T.damag D.T.open
## 1420 1419
## D.T.tab D.T.list
## 1410 1402
## D.T.affect D.wrds.n.log
## 1413 760
## D.wrds.unq.n.log D.weight.sum
## 767 767
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 767 760
## D.chrs.uppr.n.log D.dgts.n.log
## 762 1283
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 1397 1437
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 1420 1437
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 1379 1370
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 1437 1378
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 1422 1428
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 1235 1370
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1006 1383
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 1423 1365
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 1437 1436
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 1437 1437
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 1437 1437
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 1437 1437
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 1437 1437
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 1437 1429
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 1437 1437
## D.wrds.stop.n.log D.P.http
## 829 1437
## D.P.mini D.P.air
## 1418 1425
## D.P.black D.P.white
## 1433 1433
## D.P.gold D.P.spacegray
## 1436 1434
## startprice.dgt1.is9 startprice.dgt2.is9
## 871 1226
## startprice.dgt3.is9 startprice.dcm1.is9
## 1432 878
## startprice.dcm2.is9
## 996
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 760 0 0 0 0 0
## productline .grpid descr.my .lcn
## 0 NA 760 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 101 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## startprice.log10 startprice.log10 TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs
## startprice startprice 0.8705297 TRUE 0.8705297
## UniqueID UniqueID -0.1838221 TRUE 0.1838221
## startprice.log10 startprice.log10 NA TRUE NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## startprice <NA> 1 64.51613 FALSE FALSE
## UniqueID <NA> 1 100.00000 FALSE FALSE
## startprice.log10 <NA> NA NA NA NA
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## startprice FALSE <NA> NA
## UniqueID FALSE <NA> NA
## startprice.log10 NA <NA> NA
## rsp_var_raw id_var rsp_var
## startprice TRUE NA NA
## UniqueID FALSE TRUE NA
## startprice.log10 NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "selfts_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 9 select.features 6 0 0 99.700 119.314
## 10 fit.models 7 0 0 119.314 NA
## elapsed
## 9 19.614
## 10 NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
return(model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" ")))
}
get_dsp_models_df <- function() {
dsp_models_df <-
orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glb_model_evl_criteria)]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl)
nrow(subset(modelLookup(mdl$method), parameter != "parameter")))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="MFO", type=glb_model_type, trainControl.method="none",
train.method=ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars=".rnorm", rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83178 -0.13988 0.01264 0.21312 0.52840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30027 0.01971 116.72 <2e-16 ***
## .rnorm -0.01571 0.02014 -0.78 0.436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2815 on 202 degrees of freedom
## Multiple R-squared: 0.003006, Adjusted R-squared: -0.00193
## F-statistic: 0.609 on 1 and 202 DF, p-value: 0.4361
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm .rnorm 0 0.582
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit
## 1 0.003 0.003005849 0.2800985 -0.001929766
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 -0.002916031 0.325751 -0.003730747if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(mdl_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except bag & rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"## Loading required package: glmnet
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.0029 on full training set
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.29963035 -0.03228825
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.30296678 -0.29922768
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.34092839 -0.12384795
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#0
## 0.04975151 0.01163242
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad4#0
## 0.13350182 0.08609395
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPadAir#0
## 0.21319492 0.19342500
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir2#0
## 0.39888886 0.38092841
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadmini#0
## 0.52354867 -0.06038499
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini2#0
## 0.05567244 0.21522805
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini3#0
## 0.29450430 0.26659263
## prdl.descr.my.fctriPadmini3#1
## 0.46330013
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.29824347 -0.03343266
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.31002512 -0.29820021
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.33254389 -0.12268603
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#0
## 0.05716652 0.01324249
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad4#0
## 0.14098890 0.08765635
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPadAir#0
## 0.22171844 0.19507116
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir2#0
## 0.40717709 0.38271341
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadmini#0
## 0.53101012 -0.05914117
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini2#0
## 0.06305532 0.21689837
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini3#0
## 0.30284978 0.26833009
## prdl.descr.my.fctriPadmini3#1
## 0.47183006
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.1X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.765 0.012
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6982893 0.1540848 0.6653155 0.3050795 0.2711572
## max.Adj.R.sq.OOB
## 1 0.2936122# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=rcv_n_folds, trainControl.repeats=rcv_n_repeats,
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}## [1] "fitting model: Max.cor.Y.rcv.3X1.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.21 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1759578 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.6288608 0.01107871 0.0302072
## [1] "fitting model: Max.cor.Y.rcv.3X3.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.353 0.008
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1741121 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.627424 0.01143292 0.06492936
## [1] "fitting model: Max.cor.Y.rcv.3X5.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.576 0.008
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1743007 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.6240784 0.01224201 0.06926107
## [1] "fitting model: Max.cor.Y.rcv.5X1.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.368 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1677621 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.6515568 0.03663248 0.123037
## [1] "fitting model: Max.cor.Y.rcv.5X3.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.98 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1686714 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.6469703 0.0281209 0.09441314
## [1] "fitting model: Max.cor.Y.rcv.5X5.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0135 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 95 -none- numeric
## beta 1900 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 20 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31509998 -0.01900084
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.21444996 -0.30106385
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.42443179 -0.13071672
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02805693 0.04429654
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06425199 0.10918922
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.16844811 0.29235689
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35074989 0.42347736
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.07032360 -0.02015470
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.18934510 0.18968562
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.23820812 0.34733551
## [1] "max lambda < lambdaOpt:"
## (Intercept) D.weight.post.stop.sum
## 2.31399841 -0.01988630
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctriPad1#0
## 0.22080027 -0.30150292
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad2#0
## -0.41977310 -0.13059533
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad3#1
## -0.02300974 0.05040483
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#1
## 0.06604255 0.11644695
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#1
## 0.17058818 0.30012911
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#1
## 0.35347101 0.43103580
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#1
## -0.06986181 -0.01524090
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#1
## 0.19158430 0.19718522
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#1
## 0.24072494 0.35616269
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.349 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6918389 0.1694123 0.6581601 0.2970344 0.2727222
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2854343 0.6420291 0.02446019 0.09766685# Add parallel coordinates graph of glb_models_df[, glb_model_evl_criteria] to evaluate cv parameters
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
train.method="rpart",
tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.rpart"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 204
##
## CP nsplit rel error
## 1 0.267779555 0 1.0000000
## 2 0.122147887 1 0.7322204
## 3 0.092257997 2 0.6100726
## 4 0.031355949 3 0.5178146
## 5 0.029852331 4 0.4864586
## 6 0.022115538 7 0.3969016
## 7 0.009118929 8 0.3747861
## 8 0.004435417 12 0.3383104
## 9 0.004255034 13 0.3338749
## 10 0.003869161 14 0.3296199
## 11 0.000000000 15 0.3257508
##
## Variable importance
## prdl.descr.my.fctriPad1#1 D.weight.post.stop.sum
## 33 17
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPad1#0
## 15 11
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPad2#1
## 4 4
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPad2#0
## 3 3
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPadAir#0
## 3 2
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini2#0
## 1 1
## prdl.descr.my.fctriPad4#0
## 1
##
## Node number 1: 204 observations, complexity param=0.2677796
## mean=2.300305, MSE=0.0786917
## left son=2 (13 obs) right son=3 (191 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.26777960, (0 missing)
## D.weight.post.stop.sum < 6.865198 to the right, improve=0.21137590, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.14944730, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.08639560, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.03782271, (0 missing)
## Surrogate splits:
## D.weight.post.stop.sum < 6.865198 to the right, agree=0.966, adj=0.462, (0 split)
##
## Node number 2: 13 observations
## mean=1.74389, MSE=0.03685458
##
## Node number 3: 191 observations, complexity param=0.1221479
## mean=2.338176, MSE=0.05903301
## left son=6 (176 obs) right son=7 (15 obs)
## Primary splits:
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.17390690, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.15637290, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.04526808, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.03991555, (0 missing)
## D.weight.post.stop.sum < 6.526384 to the right, improve=0.03455710, (0 missing)
##
## Node number 6: 176 observations, complexity param=0.092258
## mean=2.308596, MSE=0.05231682
## left son=12 (14 obs) right son=13 (162 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.16084560, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.06235906, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.05599558, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.04921046, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.03669879, (0 missing)
##
## Node number 7: 15 observations
## mean=2.685245, MSE=0.007112716
##
## Node number 12: 14 observations
## mean=1.99655, MSE=0.0228809
##
## Node number 13: 162 observations, complexity param=0.03135595
## mean=2.335563, MSE=0.04571852
## left son=26 (19 obs) right son=27 (143 obs)
## Primary splits:
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.06796290, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.06263194, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.05316202, (0 missing)
## D.weight.post.stop.sum < 6.526384 to the right, improve=0.05165202, (0 missing)
## prdl.descr.my.fctriPad2#0 < 0.5 to the right, improve=0.04599606, (0 missing)
##
## Node number 26: 19 observations
## mean=2.18264, MSE=0.02095681
##
## Node number 27: 143 observations, complexity param=0.02985233
## mean=2.355881, MSE=0.04548853
## left son=54 (12 obs) right son=55 (131 obs)
## Primary splits:
## prdl.descr.my.fctriPad2#0 < 0.5 to the right, improve=0.06703242, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.05991690, (0 missing)
## prdl.descr.my.fctriPadmini#1 < 0.5 to the right, improve=0.05502049, (0 missing)
## prdl.descr.my.fctriPadmini#0 < 0.5 to the right, improve=0.04986111, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.04827985, (0 missing)
##
## Node number 54: 12 observations
## mean=2.173434, MSE=0.01548349
##
## Node number 55: 131 observations, complexity param=0.02985233
## mean=2.372594, MSE=0.04490856
## left son=110 (12 obs) right son=111 (119 obs)
## Primary splits:
## prdl.descr.my.fctriPadmini#1 < 0.5 to the right, improve=0.07438446, (0 missing)
## prdl.descr.my.fctriPadmini#0 < 0.5 to the right, improve=0.07292464, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.05663426, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.04328778, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.03429229, (0 missing)
##
## Node number 110: 12 observations
## mean=2.190587, MSE=0.02513259
##
## Node number 111: 119 observations, complexity param=0.02985233
## mean=2.390948, MSE=0.04322542
## left son=222 (20 obs) right son=223 (99 obs)
## Primary splits:
## prdl.descr.my.fctriPadmini#0 < 0.5 to the right, improve=0.10965110, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.05365923, (0 missing)
## prdl.descr.my.fctriPad3#1 < 0.5 to the right, improve=0.04298079, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.03816837, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.02872773, (0 missing)
##
## Node number 222: 20 observations
## mean=2.237776, MSE=0.04383634
##
## Node number 223: 99 observations, complexity param=0.02211554
## mean=2.421892, MSE=0.03740477
## left son=446 (13 obs) right son=447 (86 obs)
## Primary splits:
## prdl.descr.my.fctriPad3#1 < 0.5 to the right, improve=0.09587258, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.05179457, (0 missing)
## D.weight.post.stop.sum < 6.445331 to the right, improve=0.04204771, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.03100993, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.02034699, (0 missing)
## Surrogate splits:
## D.weight.post.stop.sum < 6.736911 to the right, agree=0.879, adj=0.077, (0 split)
##
## Node number 446: 13 observations
## mean=2.267868, MSE=0.02746932
##
## Node number 447: 86 observations, complexity param=0.009118929
## mean=2.445174, MSE=0.03477847
## left son=894 (78 obs) right son=895 (8 obs)
## Primary splits:
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.04620292, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.02250207, (0 missing)
## prdl.descr.my.fctriPad4#0 < 0.5 to the right, improve=0.01383322, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.01212732, (0 missing)
## D.weight.post.stop.sum < 6.287613 to the right, improve=0.01115617, (0 missing)
##
## Node number 894: 78 observations, complexity param=0.009118929
## mean=2.432337, MSE=0.03554595
## left son=1788 (67 obs) right son=1789 (11 obs)
## Primary splits:
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.034068780, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.020844440, (0 missing)
## D.weight.post.stop.sum < 1.674646 to the left, improve=0.019827710, (0 missing)
## prdl.descr.my.fctriPad4#0 < 0.5 to the right, improve=0.009119835, (0 missing)
## prdl.descr.my.fctrUnknown#1 < 0.5 to the left, improve=0.002300139, (0 missing)
##
## Node number 895: 8 observations
## mean=2.570342, MSE=0.01002168
##
## Node number 1788: 67 observations, complexity param=0.009118929
## mean=2.418236, MSE=0.03723298
## left son=3576 (38 obs) right son=3577 (29 obs)
## Primary splits:
## D.weight.post.stop.sum < 1.674646 to the left, improve=0.048431160, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.035594370, (0 missing)
## prdl.descr.my.fctrUnknown#1 < 0.5 to the left, improve=0.006689713, (0 missing)
## prdl.descr.my.fctriPad4#0 < 0.5 to the right, improve=0.004854735, (0 missing)
## Surrogate splits:
## prdl.descr.my.fctrUnknown#1 < 0.5 to the left, agree=0.716, adj=0.345, (0 split)
## prdl.descr.my.fctriPad4#1 < 0.5 to the left, agree=0.657, adj=0.207, (0 split)
## prdl.descr.my.fctriPadAir#1 < 0.5 to the left, agree=0.627, adj=0.138, (0 split)
## prdl.descr.my.fctriPadAir2#1 < 0.5 to the left, agree=0.627, adj=0.138, (0 split)
## prdl.descr.my.fctriPadmini2#1 < 0.5 to the left, agree=0.627, adj=0.138, (0 split)
##
## Node number 1789: 11 observations
## mean=2.518221, MSE=0.01668328
##
## Node number 3576: 38 observations, complexity param=0.009118929
## mean=2.38114, MSE=0.04386365
## left son=7152 (26 obs) right son=7153 (12 obs)
## Primary splits:
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.1392369000, (0 missing)
## prdl.descr.my.fctriPad4#0 < 0.5 to the left, improve=0.0004246329, (0 missing)
##
## Node number 3577: 29 observations, complexity param=0.004435417
## mean=2.466845, MSE=0.02437841
## left son=7154 (7 obs) right son=7155 (22 obs)
## Primary splits:
## D.weight.post.stop.sum < 6.287613 to the right, improve=0.100714100, (0 missing)
## prdl.descr.my.fctrUnknown#1 < 0.5 to the right, improve=0.002579074, (0 missing)
##
## Node number 7152: 26 observations, complexity param=0.004255034
## mean=2.328047, MSE=0.05265647
## left son=14304 (15 obs) right son=14305 (11 obs)
## Primary splits:
## prdl.descr.my.fctriPad4#0 < 0.5 to the left, improve=0.0498927, (0 missing)
##
## Node number 7153: 12 observations
## mean=2.496173, MSE=0.00547233
##
## Node number 7154: 7 observations
## mean=2.379002, MSE=0.02710827
##
## Node number 7155: 22 observations, complexity param=0.003869161
## mean=2.494796, MSE=0.02027335
## left son=14310 (15 obs) right son=14311 (7 obs)
## Primary splits:
## D.weight.post.stop.sum < 5.534654 to the left, improve=0.1392604, (0 missing)
## prdl.descr.my.fctrUnknown#1 < 0.5 to the right, improve=0.1217712, (0 missing)
## Surrogate splits:
## prdl.descr.my.fctriPadAir#1 < 0.5 to the left, agree=0.727, adj=0.143, (0 split)
##
## Node number 14304: 15 observations
## mean=2.284154, MSE=0.08122492
##
## Node number 14305: 11 observations
## mean=2.387901, MSE=0.007489811
##
## Node number 14310: 15 observations
## mean=2.458498, MSE=0.0219004
##
## Node number 14311: 7 observations
## mean=2.572577, MSE=0.007913651
##
## n= 204
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 204 16.05311000 2.300305
## 2) prdl.descr.my.fctriPad1#1>=0.5 13 0.47910960 1.743890 *
## 3) prdl.descr.my.fctriPad1#1< 0.5 191 11.27530000 2.338176
## 6) prdl.descr.my.fctriPadAir2#0< 0.5 176 9.20776000 2.308596
## 12) prdl.descr.my.fctriPad1#0>=0.5 14 0.32033260 1.996550 *
## 13) prdl.descr.my.fctriPad1#0< 0.5 162 7.40640000 2.335563
## 26) prdl.descr.my.fctriPad2#1>=0.5 19 0.39817950 2.182640 *
## 27) prdl.descr.my.fctriPad2#1< 0.5 143 6.50486000 2.355881
## 54) prdl.descr.my.fctriPad2#0>=0.5 12 0.18580180 2.173434 *
## 55) prdl.descr.my.fctriPad2#0< 0.5 131 5.88302200 2.372594
## 110) prdl.descr.my.fctriPadmini#1>=0.5 12 0.30159100 2.190587 *
## 111) prdl.descr.my.fctriPadmini#1< 0.5 119 5.14382500 2.390948
## 222) prdl.descr.my.fctriPadmini#0>=0.5 20 0.87672690 2.237776 *
## 223) prdl.descr.my.fctriPadmini#0< 0.5 99 3.70307200 2.421892
## 446) prdl.descr.my.fctriPad3#1>=0.5 13 0.35710110 2.267868 *
## 447) prdl.descr.my.fctriPad3#1< 0.5 86 2.99094800 2.445174
## 894) prdl.descr.my.fctriPadmini3#0< 0.5 78 2.77258400 2.432337
## 1788) prdl.descr.my.fctriPadmini2#0< 0.5 67 2.49461000 2.418236
## 3576) D.weight.post.stop.sum< 1.674646 38 1.66681900 2.381140
## 7152) prdl.descr.my.fctriPadAir#0< 0.5 26 1.36906800 2.328047
## 14304) prdl.descr.my.fctriPad4#0< 0.5 15 1.21837400 2.284154 *
## 14305) prdl.descr.my.fctriPad4#0>=0.5 11 0.08238792 2.387901 *
## 7153) prdl.descr.my.fctriPadAir#0>=0.5 12 0.06566796 2.496173 *
## 3577) D.weight.post.stop.sum>=1.674646 29 0.70697380 2.466845
## 7154) D.weight.post.stop.sum>=6.287613 7 0.18975790 2.379002 *
## 7155) D.weight.post.stop.sum< 6.287613 22 0.44601370 2.494796
## 14310) D.weight.post.stop.sum< 5.534654 15 0.32850610 2.458498 *
## 14311) D.weight.post.stop.sum>=5.534654 7 0.05539556 2.572577 *
## 1789) prdl.descr.my.fctriPadmini2#0>=0.5 11 0.18351610 2.518221 *
## 895) prdl.descr.my.fctriPadmini3#0>=0.5 8 0.08017341 2.570342 *
## 7) prdl.descr.my.fctriPadAir2#0>=0.5 15 0.10669070 2.685245 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.1X1.cp.0.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.377 0.027
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6742492 0.1601058 NA 0.2625824 0.2793253
## max.Adj.R.sq.OOB
## 1 NA# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="rpart")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0299 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 204
##
## CP nsplit rel error
## 1 0.26777956 0 1.0000000
## 2 0.12214789 1 0.7322204
## 3 0.09225800 2 0.6100726
## 4 0.03135595 3 0.5178146
## 5 0.02985233 4 0.4864586
##
## Variable importance
## prdl.descr.my.fctriPad1#1 D.weight.post.stop.sum
## 42 19
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPad1#0
## 19 14
## prdl.descr.my.fctriPad2#1
## 5
##
## Node number 1: 204 observations, complexity param=0.2677796
## mean=2.300305, MSE=0.0786917
## left son=2 (13 obs) right son=3 (191 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.26777960, (0 missing)
## D.weight.post.stop.sum < 6.865198 to the right, improve=0.21137590, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.14944730, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.08639560, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.03782271, (0 missing)
## Surrogate splits:
## D.weight.post.stop.sum < 6.865198 to the right, agree=0.966, adj=0.462, (0 split)
##
## Node number 2: 13 observations
## mean=1.74389, MSE=0.03685458
##
## Node number 3: 191 observations, complexity param=0.1221479
## mean=2.338176, MSE=0.05903301
## left son=6 (176 obs) right son=7 (15 obs)
## Primary splits:
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.17390690, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.15637290, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.04526808, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.03991555, (0 missing)
## D.weight.post.stop.sum < 6.526384 to the right, improve=0.03455710, (0 missing)
##
## Node number 6: 176 observations, complexity param=0.092258
## mean=2.308596, MSE=0.05231682
## left son=12 (14 obs) right son=13 (162 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.16084560, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.06235906, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.05599558, (0 missing)
## prdl.descr.my.fctriPadAir#0 < 0.5 to the left, improve=0.04921046, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.03669879, (0 missing)
##
## Node number 7: 15 observations
## mean=2.685245, MSE=0.007112716
##
## Node number 12: 14 observations
## mean=1.99655, MSE=0.0228809
##
## Node number 13: 162 observations, complexity param=0.03135595
## mean=2.335563, MSE=0.04571852
## left son=26 (19 obs) right son=27 (143 obs)
## Primary splits:
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.06796290, (0 missing)
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, improve=0.06263194, (0 missing)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, improve=0.05316202, (0 missing)
## D.weight.post.stop.sum < 6.526384 to the right, improve=0.05165202, (0 missing)
## prdl.descr.my.fctriPad2#0 < 0.5 to the right, improve=0.04599606, (0 missing)
##
## Node number 26: 19 observations
## mean=2.18264, MSE=0.02095681
##
## Node number 27: 143 observations
## mean=2.355881, MSE=0.04548853
##
## n= 204
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 204 16.0531100 2.300305
## 2) prdl.descr.my.fctriPad1#1>=0.5 13 0.4791096 1.743890 *
## 3) prdl.descr.my.fctriPad1#1< 0.5 191 11.2753000 2.338176
## 6) prdl.descr.my.fctriPadAir2#0< 0.5 176 9.2077600 2.308596
## 12) prdl.descr.my.fctriPad1#0>=0.5 14 0.3203326 1.996550 *
## 13) prdl.descr.my.fctriPad1#0< 0.5 162 7.4064000 2.335563
## 26) prdl.descr.my.fctriPad2#1>=0.5 19 0.3981795 2.182640 *
## 27) prdl.descr.my.fctriPad2#1< 0.5 143 6.5048600 2.355881 *
## 7) prdl.descr.my.fctriPadAir2#0>=0.5 15 0.1066907 2.685245 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.326 0.016
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5135414 0.2184953 NA 0.1858427 0.2934997
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 NA 0.4057368 0.02443849 0.1233202if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: Interact.High.cor.Y.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.weight.post.stop.sum,prdl.descr.my.fctr:D.weight.post.stop.sum,prdl.descr.my.fctr:startprice.dcm1.is9,prdl.descr.my.fctr:D.chrs.uppr.n.log,prdl.descr.my.fctr:D.wrds.unq.n.log"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0137 on full training set
## Length Class Mode
## a0 97 -none- numeric
## beta 9603 dgCMatrix S4
## df 97 -none- numeric
## dim 2 -none- numeric
## lambda 97 -none- numeric
## dev.ratio 97 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 99 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.3182514557
## prdl.descr.my.fctriPad1#0
## -0.2785693767
## prdl.descr.my.fctriPad1#1
## -0.0305772152
## prdl.descr.my.fctriPad2#0
## -0.0932605239
## prdl.descr.my.fctriPad4#0
## 0.0236692378
## prdl.descr.my.fctriPadAir#0
## 0.1328439445
## prdl.descr.my.fctriPadAir#1
## 0.0269801562
## prdl.descr.my.fctriPadAir2#0
## 0.3248448896
## prdl.descr.my.fctriPadAir2#1
## 0.2059905611
## prdl.descr.my.fctriPadmini#0
## -0.0172847735
## prdl.descr.my.fctriPadmini2#0
## 0.1526168311
## prdl.descr.my.fctriPadmini3#0
## 0.1959347365
## prdl.descr.my.fctriPadmini3#1
## 0.0560692303
## prdl.descr.my.fctriPad1#1:D.chrs.uppr.n.log
## -0.0034285905
## prdl.descr.my.fctriPadAir#1:D.chrs.uppr.n.log
## 0.0186035443
## prdl.descr.my.fctriPadmini3#1:D.chrs.uppr.n.log
## 0.0086566937
## D.weight.post.stop.sum:prdl.descr.my.fctrUnknown#1
## 0.0093225113
## D.weight.post.stop.sum:prdl.descr.my.fctriPad1#1
## -0.0449680738
## D.weight.post.stop.sum:prdl.descr.my.fctriPad2#1
## -0.0210607006
## D.weight.post.stop.sum:prdl.descr.my.fctriPad3#1
## -0.0048320552
## D.weight.post.stop.sum:prdl.descr.my.fctriPadAir2#1
## 0.0150293256
## D.weight.post.stop.sum:prdl.descr.my.fctriPadmini#1
## -0.0172002773
## D.weight.post.stop.sum:prdl.descr.my.fctriPadmini3#1
## 0.0029314853
## prdl.descr.my.fctriPad1#1:D.wrds.unq.n.log
## -0.0277129211
## prdl.descr.my.fctriPadAir#1:D.wrds.unq.n.log
## 0.0177660245
## prdl.descr.my.fctriPadmini2#1:D.wrds.unq.n.log
## 0.0142196997
## prdl.descr.my.fctriPadmini3#1:D.wrds.unq.n.log
## 0.0007658821
## prdl.descr.my.fctrUnknown#1:startprice.dcm1.is9
## 0.0882855833
## prdl.descr.my.fctriPad1#1:startprice.dcm1.is9
## -0.2240557378
## prdl.descr.my.fctriPad2#0:startprice.dcm1.is9
## -0.0275641694
## prdl.descr.my.fctriPadmini#0:startprice.dcm1.is9
## -0.2989060413
## prdl.descr.my.fctriPadmini3#1:startprice.dcm1.is9
## 0.0052944242
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.317801008
## prdl.descr.my.fctriPad1#0
## -0.281920958
## prdl.descr.my.fctriPad1#1
## -0.024663457
## prdl.descr.my.fctriPad2#0
## -0.095435162
## prdl.descr.my.fctriPad4#0
## 0.028160743
## prdl.descr.my.fctriPadAir#0
## 0.137256412
## prdl.descr.my.fctriPadAir#1
## 0.030004522
## prdl.descr.my.fctriPadAir2#0
## 0.329000709
## prdl.descr.my.fctriPadAir2#1
## 0.212102255
## prdl.descr.my.fctriPadmini#0
## -0.018995229
## prdl.descr.my.fctriPadmini2#0
## 0.157230403
## prdl.descr.my.fctriPadmini3#0
## 0.201322137
## prdl.descr.my.fctriPadmini3#1
## 0.058481079
## prdl.descr.my.fctriPad1#1:D.chrs.uppr.n.log
## -0.002962480
## prdl.descr.my.fctriPadAir#1:D.chrs.uppr.n.log
## 0.019349314
## prdl.descr.my.fctriPadmini3#1:D.chrs.uppr.n.log
## 0.009728418
## D.weight.post.stop.sum:prdl.descr.my.fctrUnknown#1
## 0.009889442
## D.weight.post.stop.sum:prdl.descr.my.fctriPad1#1
## -0.046243771
## D.weight.post.stop.sum:prdl.descr.my.fctriPad2#1
## -0.021562294
## D.weight.post.stop.sum:prdl.descr.my.fctriPad3#1
## -0.005449527
## D.weight.post.stop.sum:prdl.descr.my.fctriPadAir2#1
## 0.015306334
## D.weight.post.stop.sum:prdl.descr.my.fctriPadmini#1
## -0.017864005
## D.weight.post.stop.sum:prdl.descr.my.fctriPadmini3#1
## 0.003656021
## prdl.descr.my.fctriPad1#1:D.wrds.unq.n.log
## -0.028538446
## prdl.descr.my.fctriPadAir#1:D.wrds.unq.n.log
## 0.018598532
## prdl.descr.my.fctriPadmini2#1:D.wrds.unq.n.log
## 0.018145722
## prdl.descr.my.fctriPadmini3#1:D.wrds.unq.n.log
## 0.001157485
## prdl.descr.my.fctrUnknown#1:startprice.dcm1.is9
## 0.091599224
## prdl.descr.my.fctriPad1#1:startprice.dcm1.is9
## -0.225933918
## prdl.descr.my.fctriPad2#0:startprice.dcm1.is9
## -0.032851782
## prdl.descr.my.fctriPadmini#0:startprice.dcm1.is9
## -0.306710191
## prdl.descr.my.fctriPadmini3#1:startprice.dcm1.is9
## 0.007460234
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Interact.High.cor.Y.glmnet
## feats
## 1 prdl.descr.my.fctr,D.weight.post.stop.sum,prdl.descr.my.fctr:D.weight.post.stop.sum,prdl.descr.my.fctr:startprice.dcm1.is9,prdl.descr.my.fctr:D.chrs.uppr.n.log,prdl.descr.my.fctr:D.wrds.unq.n.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.224 0.046
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.7082962 0.1793821 0.4306167 0.2806903 0.2758745
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.217838 0.60429 0.01050939 0.08091175# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Low.cor.X.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00333 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 10764 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 138 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.412207258
## D.chrs.pnct11.n.log
## -0.003522085
## D.ratio.weight.sum.wrds.n
## -0.015084699
## color.fctrSpace Gray
## 0.011874939
## color.fctrWhite
## 0.035977222
## condition.fctrFor parts or not working
## -0.364964351
## condition.fctrNew
## 0.156915781
## condition.fctrNew other (see details)
## 0.055657294
## prdl.descr.my.fctrUnknown#1
## 0.026757648
## prdl.descr.my.fctriPad1#0
## -0.359382832
## prdl.descr.my.fctriPad1#1
## -0.338420550
## prdl.descr.my.fctriPad2#0
## -0.119388133
## prdl.descr.my.fctriPad2#1
## -0.093149356
## prdl.descr.my.fctriPad3#1
## -0.060683098
## prdl.descr.my.fctriPad4#0
## 0.001029747
## prdl.descr.my.fctriPadAir#0
## 0.135656000
## prdl.descr.my.fctriPadAir#1
## 0.155660101
## prdl.descr.my.fctriPadAir2#0
## 0.228552301
## prdl.descr.my.fctriPadAir2#1
## 0.224789075
## prdl.descr.my.fctriPadmini#0
## -0.080463100
## prdl.descr.my.fctriPadmini#1
## -0.106831484
## prdl.descr.my.fctriPadmini2#0
## 0.004738705
## prdl.descr.my.fctriPadmini2#1
## 0.003289255
## prdl.descr.my.fctriPadmini3#0
## 0.084524654
## prdl.descr.my.fctriPadmini3#1
## 0.134961053
## startprice.dgt2.is9
## 0.023758934
## storage.fctr16
## -0.128539765
## storage.fctr32
## -0.052639732
## cellular.fctr0:carrier.fctrNone
## -0.006058987
## cellular.fctr1:carrier.fctrSprint
## 0.029486687
## cellular.fctr1:carrier.fctrT-Mobile
## 0.173495112
## cellular.fctr1:carrier.fctrUnknown
## 0.019212675
## cellular.fctr1:carrier.fctrVerizon
## 0.056365058
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.089856089
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.021774765
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.011577722
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.112605180
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.4118153692
## D.chrs.pnct11.n.log
## -0.0046344700
## D.ratio.weight.sum.wrds.n
## -0.0155442716
## color.fctrSpace Gray
## 0.0123497355
## color.fctrWhite
## 0.0361859933
## condition.fctrFor parts or not working
## -0.3647337399
## condition.fctrNew
## 0.1568896336
## condition.fctrNew other (see details)
## 0.0574654272
## prdl.descr.my.fctrUnknown#1
## 0.0310827125
## prdl.descr.my.fctriPad1#0
## -0.3596091724
## prdl.descr.my.fctriPad1#1
## -0.3378868769
## prdl.descr.my.fctriPad2#0
## -0.1199261327
## prdl.descr.my.fctriPad2#1
## -0.0932806218
## prdl.descr.my.fctriPad3#1
## -0.0618760405
## prdl.descr.my.fctriPad4#0
## 0.0026802156
## prdl.descr.my.fctriPadAir#0
## 0.1373060680
## prdl.descr.my.fctriPadAir#1
## 0.1587892604
## prdl.descr.my.fctriPadAir2#0
## 0.2303221576
## prdl.descr.my.fctriPadAir2#1
## 0.2266486321
## prdl.descr.my.fctriPadmini#0
## -0.0808985947
## prdl.descr.my.fctriPadmini#1
## -0.1067445831
## prdl.descr.my.fctriPadmini2#0
## 0.0056981360
## prdl.descr.my.fctriPadmini2#1
## 0.0062350032
## prdl.descr.my.fctriPadmini3#0
## 0.0868525342
## prdl.descr.my.fctriPadmini3#1
## 0.1406121977
## startprice.dcm1.is9
## -0.0009186898
## startprice.dgt2.is9
## 0.0249778097
## storage.fctr16
## -0.1292416835
## storage.fctr32
## -0.0538605629
## cellular.fctr0:carrier.fctrNone
## -0.0061307745
## cellular.fctr1:carrier.fctrSprint
## 0.0314857281
## cellular.fctr1:carrier.fctrT-Mobile
## 0.1764557942
## cellular.fctr1:carrier.fctrUnknown
## 0.0193754167
## cellular.fctr1:carrier.fctrVerizon
## 0.0582195623
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## -0.0049960590
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0957327989
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0235337891
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0155723132
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.1168123139
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Low.cor.X.glmnet
## feats
## 1 prdl.descr.my.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.767 0.011
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8953388 0.1135832 0.6731351 0.3295102 0.2663481
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2449328 0.8378099 0.007517978 0.03713866rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 10 fit.models 7 0 0 119.314 158.013 38.699
## 11 fit.models 7 1 1 158.014 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 161.552 NA NA#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc=TRUE, label.minor="setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
# !_sp
# interact_vars_vctr <- c(
# "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio",
# "D.npnct15.log", "D.npnct03.log", "D.wrds.n.log", "D.chrs.n.log")
# indep_vars <- union(setdiff(indep_vars, interact_vars_vctr),
# paste(glb_category_var, interact_vars_vctr,
# sep=ifelse(grepl("\\.fctr", glb_category_var), "*", ".fctr*")))
# indep_vars <- union(setdiff(indep_vars,
# c("startprice.log.diff", "startprice.unit9", "biddable", "cellular.fctr", "carrier.fctr")),
# c("startprice.log.diff*biddable", "startprice.unit9*biddable", "cellular.fctr*carrier.fctr"))
###
# _sp only
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(), glb_models_df)
dsp_models_df <- subset(dsp_models_df, grepl(".glmnet", id, fixed=TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select most importance feature
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <- paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glb_interaction_only_feats_lst)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
importance_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df, importance > importance_cutoff)), -1)
interact_vars <- myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <- interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
indep_vars <- setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var), sep="*"))
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
glb_mdl_family_lst[[mdl_id_pfx]] <- glb_mdl_family_lst[["Best.Interact"]]
}
}
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
mdl_methods <- glb_mdl_methods else
mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method)),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}
} ## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 161.552 161.562
## 2 fit.models_1_RFE.X 2 0 setup 161.562 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_RFE.X 2 0 setup 161.562 161.569
## 3 fit.models_1_RFE.X 2 1 glm 161.569 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "fitting model: RFE.X.glm"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 106
## Warning: not plotting observations with leverage one:
## 106
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.313219 -0.039582 -0.000728 0.044511 0.261090
##
## Coefficients: (81 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 3.5476728 2.1067114
## .rnorm 0.0017122 0.0087646
## D.chrs.n.log -0.5184414 0.4149299
## D.chrs.pnct11.n.log -0.0586199 0.0381193
## D.chrs.pnct13.n.log 0.0037365 0.0390477
## D.chrs.uppr.n.log 0.5791042 0.3523899
## D.ratio.weight.sum.wrds.n -0.0234699 0.0330529
## D.ratio.wrds.stop.n.wrds.n -0.1762770 0.1655766
## D.terms.post.stop.n.log -0.3600509 0.6013656
## D.weight.post.stem.sum 0.0993300 0.3628484
## D.weight.post.stop.sum -0.0960339 0.3523124
## D.weight.sum.stem.stop.Ratio -0.9062240 2.1300208
## D.wrds.n.log -0.0119878 0.1650633
## D.wrds.unq.n.log 0.3659322 0.6159951
## cellular.fctr1 -0.0131861 0.0261490
## cellular.fctrUnknown 0.0369164 0.0468812
## color.fctrGold 0.0069764 0.0582195
## `color.fctrSpace Gray` 0.0400291 0.0368761
## color.fctrUnknown 0.0188142 0.0230303
## color.fctrWhite 0.0506530 0.0280680
## `condition.fctrFor parts or not working` -0.3685874 0.0349586
## `condition.fctrManufacturer refurbished` -0.0256583 0.0501409
## condition.fctrNew 0.1550475 0.0307831
## `condition.fctrNew other (see details)` 0.0900502 0.0423057
## `condition.fctrSeller refurbished` -0.0189031 0.0406052
## `prdl.descr.my.fctrUnknown#1` -0.0899080 0.2684341
## `prdl.descr.my.fctriPad1#0` -0.3505329 0.0507492
## `prdl.descr.my.fctriPad1#1` -0.5395698 0.2733217
## `prdl.descr.my.fctriPad2#0` -0.1065933 0.0528469
## `prdl.descr.my.fctriPad2#1` -0.3305037 0.2667949
## `prdl.descr.my.fctriPad3#0` -0.0075691 0.0731857
## `prdl.descr.my.fctriPad3#1` -0.3007396 0.2829833
## `prdl.descr.my.fctriPad4#0` 0.0413625 0.0499928
## `prdl.descr.my.fctriPad4#1` -0.0953260 0.2862815
## `prdl.descr.my.fctriPadAir#0` 0.1572930 0.0505474
## `prdl.descr.my.fctriPadAir#1` -0.0117543 0.2608515
## `prdl.descr.my.fctriPadAir2#0` 0.2336502 0.0522468
## `prdl.descr.my.fctriPadAir2#1` -0.0581970 0.2910690
## `prdl.descr.my.fctriPadmini#0` -0.0836182 0.0452411
## `prdl.descr.my.fctriPadmini#1` -0.2929186 0.2684570
## `prdl.descr.my.fctriPadmini2#0` -0.0003345 0.0543461
## `prdl.descr.my.fctriPadmini2#1` -0.1576321 0.2771633
## `prdl.descr.my.fctriPadmini3#0` 0.1054937 0.0607805
## `prdl.descr.my.fctriPadmini3#1` -0.0546854 0.2925945
## startprice.dcm1.is9 -0.0365801 0.0306567
## startprice.dcm2.is9 0.0199601 0.0327820
## startprice.dgt2.is9 0.0417915 0.0255029
## storage.fctr16 -0.2108537 0.0500656
## storage.fctr32 -0.1427371 0.0551693
## storage.fctr64 -0.0657102 0.0511000
## storage.fctrUnknown -0.1249839 0.0686882
## `cellular.fctr0:carrier.fctrNone` NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA
## `cellular.fctr1:carrier.fctrSprint` 0.0352458 0.0529591
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.2435699 0.0785231
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA
## `cellular.fctr1:carrier.fctrUnknown` 0.0173914 0.0520608
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA
## `cellular.fctr1:carrier.fctrVerizon` 0.0715921 0.0355465
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr2` -0.0719438 0.0853216
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr2` 0.0308679 0.0780401
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr2` 0.1986216 0.0862959
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr2` -0.1767065 0.1014318
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2` 0.1837042 0.1314072
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2` -0.0279473 0.1204091
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr3` 0.0734626 0.0774831
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr3` 0.0390041 0.0973955
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr3` 0.0391016 0.0888987
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr4` 0.0095931 0.1006761
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) 1.684 0.09443 .
## .rnorm 0.195 0.84540
## D.chrs.n.log -1.249 0.21359
## D.chrs.pnct11.n.log -1.538 0.12637
## D.chrs.pnct13.n.log 0.096 0.92391
## D.chrs.uppr.n.log 1.643 0.10257
## D.ratio.weight.sum.wrds.n -0.710 0.47885
## D.ratio.wrds.stop.n.wrds.n -1.065 0.28889
## D.terms.post.stop.n.log -0.599 0.55033
## D.weight.post.stem.sum 0.274 0.78468
## D.weight.post.stop.sum -0.273 0.78558
## D.weight.sum.stem.stop.Ratio -0.425 0.67116
## D.wrds.n.log -0.073 0.94221
## D.wrds.unq.n.log 0.594 0.55344
## cellular.fctr1 -0.504 0.61487
## cellular.fctrUnknown 0.787 0.43236
## color.fctrGold 0.120 0.90479
## `color.fctrSpace Gray` 1.086 0.27958
## color.fctrUnknown 0.817 0.41536
## color.fctrWhite 1.805 0.07329 .
## `condition.fctrFor parts or not working` -10.544 < 2e-16 ***
## `condition.fctrManufacturer refurbished` -0.512 0.60966
## condition.fctrNew 5.037 1.45e-06 ***
## `condition.fctrNew other (see details)` 2.129 0.03505 *
## `condition.fctrSeller refurbished` -0.466 0.64228
## `prdl.descr.my.fctrUnknown#1` -0.335 0.73818
## `prdl.descr.my.fctriPad1#0` -6.907 1.63e-10 ***
## `prdl.descr.my.fctriPad1#1` -1.974 0.05035 .
## `prdl.descr.my.fctriPad2#0` -2.017 0.04562 *
## `prdl.descr.my.fctriPad2#1` -1.239 0.21751
## `prdl.descr.my.fctriPad3#0` -0.103 0.91778
## `prdl.descr.my.fctriPad3#1` -1.063 0.28974
## `prdl.descr.my.fctriPad4#0` 0.827 0.40945
## `prdl.descr.my.fctriPad4#1` -0.333 0.73965
## `prdl.descr.my.fctriPadAir#0` 3.112 0.00226 **
## `prdl.descr.my.fctriPadAir#1` -0.045 0.96412
## `prdl.descr.my.fctriPadAir2#0` 4.472 1.60e-05 ***
## `prdl.descr.my.fctriPadAir2#1` -0.200 0.84182
## `prdl.descr.my.fctriPadmini#0` -1.848 0.06669 .
## `prdl.descr.my.fctriPadmini#1` -1.091 0.27711
## `prdl.descr.my.fctriPadmini2#0` -0.006 0.99510
## `prdl.descr.my.fctriPadmini2#1` -0.569 0.57045
## `prdl.descr.my.fctriPadmini3#0` 1.736 0.08484 .
## `prdl.descr.my.fctriPadmini3#1` -0.187 0.85201
## startprice.dcm1.is9 -1.193 0.23482
## startprice.dcm2.is9 0.609 0.54360
## startprice.dgt2.is9 1.639 0.10354
## storage.fctr16 -4.212 4.53e-05 ***
## storage.fctr32 -2.587 0.01070 *
## storage.fctr64 -1.286 0.20061
## storage.fctrUnknown -1.820 0.07097 .
## `cellular.fctr0:carrier.fctrNone` NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA
## `cellular.fctr1:carrier.fctrSprint` 0.666 0.50682
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` 3.102 0.00233 **
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA
## `cellular.fctr1:carrier.fctrUnknown` 0.334 0.73884
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA
## `cellular.fctr1:carrier.fctrVerizon` 2.014 0.04594 *
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr2` -0.843 0.40056
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr2` 0.396 0.69305
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr2` 2.302 0.02284 *
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr2` -1.742 0.08370 .
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2` 1.398 0.16435
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2` -0.232 0.81680
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr3` 0.948 0.34472
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr3` 0.400 0.68942
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr3` 0.440 0.66073
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr4` 0.095 0.92422
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4` NA NA
## `prdl.descr.my.fctrUnknown#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctrUnknown#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad1#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad1#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad3#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad3#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad4#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPad4#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5` NA NA
## `prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01038566)
##
## Null deviance: 16.0531 on 203 degrees of freedom
## Residual deviance: 1.4436 on 139 degrees of freedom
## AIC: -299.07
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## id
## 1 RFE.X.glm
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.379 0.055
## max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1 0.9100731 0.1542425 -299.0721 0.8686679 0.2920533
## min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1 0.2736868 0.2532617 0.7514847 0.03876787
## max.RsquaredSD.fit
## 1 0.08601771
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_RFE.X 2 1 glm 161.569 164.775
## 4 fit.models_1_RFE.X 2 2 glmnet 164.776 NA
## elapsed
## 3 3.206
## 4 NA
## [1] "fitting model: RFE.X.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00333 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 11310 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 145 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.412207258
## D.chrs.pnct11.n.log
## -0.003522085
## D.ratio.weight.sum.wrds.n
## -0.015084699
## color.fctrSpace Gray
## 0.011874939
## color.fctrWhite
## 0.035977222
## condition.fctrFor parts or not working
## -0.364964351
## condition.fctrNew
## 0.156915781
## condition.fctrNew other (see details)
## 0.055657294
## prdl.descr.my.fctrUnknown#1
## 0.026757648
## prdl.descr.my.fctriPad1#0
## -0.359382832
## prdl.descr.my.fctriPad1#1
## -0.338420550
## prdl.descr.my.fctriPad2#0
## -0.119388133
## prdl.descr.my.fctriPad2#1
## -0.093149356
## prdl.descr.my.fctriPad3#1
## -0.060683098
## prdl.descr.my.fctriPad4#0
## 0.001029747
## prdl.descr.my.fctriPadAir#0
## 0.135656000
## prdl.descr.my.fctriPadAir#1
## 0.155660101
## prdl.descr.my.fctriPadAir2#0
## 0.228552301
## prdl.descr.my.fctriPadAir2#1
## 0.224789075
## prdl.descr.my.fctriPadmini#0
## -0.080463100
## prdl.descr.my.fctriPadmini#1
## -0.106831484
## prdl.descr.my.fctriPadmini2#0
## 0.004738705
## prdl.descr.my.fctriPadmini2#1
## 0.003289255
## prdl.descr.my.fctriPadmini3#0
## 0.084524654
## prdl.descr.my.fctriPadmini3#1
## 0.134961053
## startprice.dgt2.is9
## 0.023758934
## storage.fctr16
## -0.128539765
## storage.fctr32
## -0.052639732
## cellular.fctr0:carrier.fctrNone
## -0.006058987
## cellular.fctr1:carrier.fctrSprint
## 0.029486687
## cellular.fctr1:carrier.fctrT-Mobile
## 0.173495112
## cellular.fctr1:carrier.fctrUnknown
## 0.019212675
## cellular.fctr1:carrier.fctrVerizon
## 0.056365058
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.089856089
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.021774765
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.011577722
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.112605180
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.4118153692
## D.chrs.pnct11.n.log
## -0.0046344700
## D.ratio.weight.sum.wrds.n
## -0.0155442716
## color.fctrSpace Gray
## 0.0123497355
## color.fctrWhite
## 0.0361859933
## condition.fctrFor parts or not working
## -0.3647337399
## condition.fctrNew
## 0.1568896336
## condition.fctrNew other (see details)
## 0.0574654272
## prdl.descr.my.fctrUnknown#1
## 0.0310827125
## prdl.descr.my.fctriPad1#0
## -0.3596091724
## prdl.descr.my.fctriPad1#1
## -0.3378868769
## prdl.descr.my.fctriPad2#0
## -0.1199261327
## prdl.descr.my.fctriPad2#1
## -0.0932806218
## prdl.descr.my.fctriPad3#1
## -0.0618760405
## prdl.descr.my.fctriPad4#0
## 0.0026802156
## prdl.descr.my.fctriPadAir#0
## 0.1373060680
## prdl.descr.my.fctriPadAir#1
## 0.1587892604
## prdl.descr.my.fctriPadAir2#0
## 0.2303221576
## prdl.descr.my.fctriPadAir2#1
## 0.2266486321
## prdl.descr.my.fctriPadmini#0
## -0.0808985947
## prdl.descr.my.fctriPadmini#1
## -0.1067445831
## prdl.descr.my.fctriPadmini2#0
## 0.0056981360
## prdl.descr.my.fctriPadmini2#1
## 0.0062350032
## prdl.descr.my.fctriPadmini3#0
## 0.0868525342
## prdl.descr.my.fctriPadmini3#1
## 0.1406121977
## startprice.dcm1.is9
## -0.0009186898
## startprice.dgt2.is9
## 0.0249778097
## storage.fctr16
## -0.1292416835
## storage.fctr32
## -0.0538605629
## cellular.fctr0:carrier.fctrNone
## -0.0061307745
## cellular.fctr1:carrier.fctrSprint
## 0.0314857281
## cellular.fctr1:carrier.fctrT-Mobile
## 0.1764557942
## cellular.fctr1:carrier.fctrUnknown
## 0.0193754167
## cellular.fctr1:carrier.fctrVerizon
## 0.0582195623
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## -0.0049960590
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0957327989
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0235337891
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0155723132
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.1168123139
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.glmnet
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.793 0.012
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8953388 0.1133377 0.6336859 0.3295102 0.2663481
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2400704 0.8385459 0.007592452 0.03745714
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_RFE.X 2 2 glmnet 164.776 168.637
## 5 fit.models_1_RFE.X 2 3 rpart 168.637 NA
## elapsed
## 4 3.861
## 5 NA
## [1] "fitting model: RFE.X.rpart"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.025 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 204
##
## CP nsplit rel error
## 1 0.35258370 0 1.0000000
## 2 0.12251081 1 0.6474163
## 3 0.09395662 2 0.5249055
## 4 0.06012801 3 0.4309489
## 5 0.03230142 4 0.3708209
## 6 0.02500000 5 0.3385194
##
## Variable importance
## condition.fctrFor parts or not working
## 30
## D.weight.post.stem.sum
## 13
## D.weight.post.stop.sum
## 12
## prdl.descr.my.fctriPad1#0
## 10
## condition.fctrNew
## 8
## prdl.descr.my.fctriPad1#1
## 7
## storage.fctr16
## 5
## prdl.descr.my.fctriPadmini3#0
## 2
## storage.fctr64
## 2
## storage.fctr32
## 2
## D.ratio.weight.sum.wrds.n
## 2
## cellular.fctr0:carrier.fctrNone
## 2
## D.chrs.n.log
## 1
## D.chrs.uppr.n.log
## 1
## storage.fctrUnknown
## 1
## cellular.fctrUnknown
## 1
##
## Node number 1: 204 observations, complexity param=0.3525837
## mean=2.300305, MSE=0.0786917
## left son=2 (21 obs) right son=3 (183 obs)
## Primary splits:
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.3525837, (0 missing)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.2677796, (0 missing)
## D.weight.post.stem.sum < 6.736911 to the right, improve=0.2450332, (0 missing)
## D.weight.post.stop.sum < 6.865198 to the right, improve=0.2113759, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.1749030, (0 missing)
## Surrogate splits:
## D.weight.post.stem.sum < 6.736911 to the right, agree=0.936, adj=0.381, (0 split)
## D.weight.post.stop.sum < 6.780795 to the right, agree=0.931, adj=0.333, (0 split)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, agree=0.912, adj=0.143, (0 split)
##
## Node number 2: 21 observations, complexity param=0.03230142
## mean=1.808592, MSE=0.05699919
## left son=4 (8 obs) right son=5 (13 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.4332042, (0 missing)
## D.weight.post.stem.sum < 6.833373 to the right, improve=0.3522119, (0 missing)
## D.weight.post.stop.sum < 6.840693 to the right, improve=0.3522119, (0 missing)
## startprice.dcm1.is9 < 0.5 to the right, improve=0.3511914, (0 missing)
## D.ratio.weight.sum.wrds.n < 0.5211138 to the right, improve=0.3231283, (0 missing)
## Surrogate splits:
## D.weight.post.stem.sum < 6.833373 to the right, agree=0.905, adj=0.750, (0 split)
## D.weight.post.stop.sum < 6.840693 to the right, agree=0.905, adj=0.750, (0 split)
## D.ratio.weight.sum.wrds.n < 0.5211138 to the right, agree=0.857, adj=0.625, (0 split)
## D.chrs.n.log < 4.016342 to the right, agree=0.810, adj=0.500, (0 split)
## D.chrs.uppr.n.log < 3.849921 to the right, agree=0.810, adj=0.500, (0 split)
##
## Node number 3: 183 observations, complexity param=0.1225108
## mean=2.356731, MSE=0.0502517
## left son=6 (14 obs) right son=7 (169 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.21386110, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.19949250, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.19175240, (0 missing)
## color.fctrGold < 0.5 to the left, improve=0.07204825, (0 missing)
## storage.fctr16 < 0.5 to the right, improve=0.07028365, (0 missing)
##
## Node number 4: 8 observations
## mean=1.60828, MSE=0.01116213
##
## Node number 5: 13 observations
## mean=1.931861, MSE=0.04531907
##
## Node number 6: 14 observations
## mean=1.99655, MSE=0.0228809
##
## Node number 7: 169 observations, complexity param=0.09395662
## mean=2.386568, MSE=0.04088195
## left son=14 (140 obs) right son=15 (29 obs)
## Primary splits:
## condition.fctrNew < 0.5 to the left, improve=0.21830730, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.21254120, (0 missing)
## storage.fctr16 < 0.5 to the right, improve=0.11433630, (0 missing)
## D.weight.post.stop.sum < 4.380553 to the right, improve=0.11064680, (0 missing)
## D.ratio.wrds.stop.n.wrds.n < 0.6095238 to the left, improve=0.09814526, (0 missing)
## Surrogate splits:
## prdl.descr.my.fctriPadmini3#0 < 0.5 to the left, agree=0.876, adj=0.276, (0 split)
## color.fctrGold < 0.5 to the left, agree=0.834, adj=0.034, (0 split)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, agree=0.834, adj=0.034, (0 split)
## prdl.descr.my.fctriPadmini2#0 < 0.5 to the left, agree=0.834, adj=0.034, (0 split)
##
## Node number 14: 140 observations, complexity param=0.06012801
## mean=2.343571, MSE=0.03516931
## left son=28 (70 obs) right son=29 (70 obs)
## Primary splits:
## storage.fctr16 < 0.5 to the right, improve=0.19603970, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.17319420, (0 missing)
## storage.fctr64 < 0.5 to the left, improve=0.12094280, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.05724294, (0 missing)
## D.weight.post.stop.sum < 4.450969 to the right, improve=0.05524024, (0 missing)
## Surrogate splits:
## storage.fctr64 < 0.5 to the left, agree=0.693, adj=0.386, (0 split)
## storage.fctr32 < 0.5 to the left, agree=0.671, adj=0.343, (0 split)
## cellular.fctr0:carrier.fctrNone < 0.5 to the right, agree=0.657, adj=0.314, (0 split)
## storage.fctrUnknown < 0.5 to the left, agree=0.621, adj=0.243, (0 split)
## cellular.fctrUnknown < 0.5 to the left, agree=0.607, adj=0.214, (0 split)
##
## Node number 15: 29 observations
## mean=2.594138, MSE=0.01644998
##
## Node number 28: 70 observations
## mean=2.260538, MSE=0.0226133
##
## Node number 29: 70 observations
## mean=2.426605, MSE=0.03393616
##
## n= 204
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 204 16.05311000 2.300305
## 2) condition.fctrFor parts or not working>=0.5 21 1.19698300 1.808592
## 4) prdl.descr.my.fctriPad1#1>=0.5 8 0.08929706 1.608280 *
## 5) prdl.descr.my.fctriPad1#1< 0.5 13 0.58914780 1.931861 *
## 3) condition.fctrFor parts or not working< 0.5 183 9.19606100 2.356731
## 6) prdl.descr.my.fctriPad1#0>=0.5 14 0.32033260 1.996550 *
## 7) prdl.descr.my.fctriPad1#0< 0.5 169 6.90904900 2.386568
## 14) condition.fctrNew< 0.5 140 4.92370400 2.343571
## 28) storage.fctr16>=0.5 70 1.58293100 2.260538 *
## 29) storage.fctr16< 0.5 70 2.37553100 2.426605 *
## 15) condition.fctrNew>=0.5 29 0.47704930 2.594138 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.rpart
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.792 0.053
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6614806 0.1770237 NA 0.1571164 0.2986327
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 NA 0.6109292 0.005823641 0.08368777
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_RFE.X 2 3 rpart 168.637 172.84
## 6 fit.models_1_RFE.X 2 4 gbm 172.841 NA
## elapsed
## 5 4.203
## 6 NA
## [1] "fitting model: RFE.X.gbm"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Loading required package: gbm
## Loading required package: splines
## Loaded gbm 2.1.1## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 300, interaction.depth = 1, shrinkage = 0.15, n.minobsinnode = 10 on full training set## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 51: cellular.fctr1:carrier.fctrNone has no
## variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 52: cellular.fctrUnknown:carrier.fctrNone has
## no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 53: cellular.fctr0:carrier.fctrSprint has no
## variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 55: cellular.fctrUnknown:carrier.fctrSprint
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 56: cellular.fctr0:carrier.fctrT-Mobile has no
## variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 58: cellular.fctrUnknown:carrier.fctrT-Mobile
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 59: cellular.fctr0:carrier.fctrUnknown has no
## variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 62: cellular.fctr0:carrier.fctrVerizon has no
## variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 64: cellular.fctrUnknown:carrier.fctrVerizon
## has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 65:
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 67: prdl.descr.my.fctriPad1#0:.clusterid.fctr2
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 68: prdl.descr.my.fctriPad1#1:.clusterid.fctr2
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 69: prdl.descr.my.fctriPad2#0:.clusterid.fctr2
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 71: prdl.descr.my.fctriPad3#0:.clusterid.fctr2
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 73: prdl.descr.my.fctriPad4#0:.clusterid.fctr2
## has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 75:
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 76:
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 77:
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 79:
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 80:
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 81:
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 83:
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 84:
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 85:
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 86:
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 87: prdl.descr.my.fctriPad1#0:.clusterid.fctr3
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 88: prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 89: prdl.descr.my.fctriPad2#0:.clusterid.fctr3
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 91: prdl.descr.my.fctriPad3#0:.clusterid.fctr3
## has no variation.## Warning in gbm.fit(x = structure(c(0, 4.53259949315326, 0,
## 4.59511985013459, : variable 93: prdl.descr.my.fctriPad4#0:.clusterid.fctr3
## has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 95:
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 96:
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 97:
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 98:
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 99:
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 101:
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 102:
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 103:
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 104:
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 105:
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 106:
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 107:
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 109:
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 110:
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 111:
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 112:
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 113:
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 114:
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 115:
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 116:
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 117:
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 118:
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 119:
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 120:
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 121:
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 122:
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 123:
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 124:
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 125:
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 126:
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 127:
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 128:
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 129:
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 130:
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 131:
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 132:
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 133:
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 134:
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 135:
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 136:
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 137:
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 138:
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 139:
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 140:
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 141:
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 142:
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 143:
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5 has no variation.## Warning in gbm.fit(x = structure(c(0,
## 4.53259949315326, 0, 4.59511985013459, : variable 144:
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5 has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.0694 nan 0.1500 0.0055
## 2 0.0663 nan 0.1500 0.0013
## 3 0.0606 nan 0.1500 0.0044
## 4 0.0573 nan 0.1500 0.0022
## 5 0.0553 nan 0.1500 0.0018
## 6 0.0523 nan 0.1500 0.0033
## 7 0.0493 nan 0.1500 0.0011
## 8 0.0481 nan 0.1500 0.0005
## 9 0.0461 nan 0.1500 0.0010
## 10 0.0453 nan 0.1500 -0.0002
## 20 0.0369 nan 0.1500 0.0000
## 40 0.0275 nan 0.1500 0.0001
## 60 0.0233 nan 0.1500 -0.0003
## 80 0.0218 nan 0.1500 -0.0001
## 100 0.0176 nan 0.1500 0.0002
## 120 0.0155 nan 0.1500 -0.0002
## 140 0.0142 nan 0.1500 -0.0001
## 160 0.0134 nan 0.1500 -0.0001
## 180 0.0129 nan 0.1500 -0.0002
## 200 0.0125 nan 0.1500 -0.0001
## 220 0.0120 nan 0.1500 -0.0001
## 240 0.0115 nan 0.1500 -0.0000
## 260 0.0111 nan 0.1500 -0.0001
## 280 0.0110 nan 0.1500 -0.0001
## 300 0.0107 nan 0.1500 -0.0001## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.trees## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: interaction.depth## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.minobsinnode
## var
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## prdl.descr.my.fctriPad1#0 prdl.descr.my.fctriPad1#0
## condition.fctrNew condition.fctrNew
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#0
## storage.fctr16 storage.fctr16
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n
## D.weight.post.stop.sum D.weight.post.stop.sum
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad1#1
## D.weight.post.stem.sum D.weight.post.stem.sum
## color.fctrWhite color.fctrWhite
## D.terms.post.stop.n.log D.terms.post.stop.n.log
## storage.fctr32 storage.fctr32
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#0
## color.fctrSpace Gray color.fctrSpace Gray
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio
## cellular.fctrUnknown cellular.fctrUnknown
## storage.fctr64 storage.fctr64
## D.chrs.n.log D.chrs.n.log
## D.wrds.n.log D.wrds.n.log
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#0
## cellular.fctr1 cellular.fctr1
## color.fctrUnknown color.fctrUnknown
## D.chrs.uppr.n.log D.chrs.uppr.n.log
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## D.wrds.unq.n.log D.wrds.unq.n.log
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad2#1
## startprice.dcm2.is9 startprice.dcm2.is9
## prdl.descr.my.fctriPad2#0 prdl.descr.my.fctriPad2#0
## startprice.dgt2.is9 startprice.dgt2.is9
## startprice.dcm1.is9 startprice.dcm1.is9
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log
## storage.fctrUnknown storage.fctrUnknown
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini#1
## color.fctrGold color.fctrGold
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctrUnknown#1
## prdl.descr.my.fctriPad3#0 prdl.descr.my.fctriPad3#0
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad3#1
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#0
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPad4#1
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir#1
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadAir2#1
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#0
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini2#1
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#0
## prdl.descr.my.fctriPadmini3#1 prdl.descr.my.fctriPadmini3#1
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2 prdl.descr.my.fctrUnknown#0:.clusterid.fctr2
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2 prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2 prdl.descr.my.fctriPad1#0:.clusterid.fctr2
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2 prdl.descr.my.fctriPad1#1:.clusterid.fctr2
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2 prdl.descr.my.fctriPad2#0:.clusterid.fctr2
## prdl.descr.my.fctriPad2#1:.clusterid.fctr2 prdl.descr.my.fctriPad2#1:.clusterid.fctr2
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2 prdl.descr.my.fctriPad3#0:.clusterid.fctr2
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2 prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2 prdl.descr.my.fctriPad4#0:.clusterid.fctr2
## prdl.descr.my.fctriPad4#1:.clusterid.fctr2 prdl.descr.my.fctriPad4#1:.clusterid.fctr2
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2 prdl.descr.my.fctriPadAir#0:.clusterid.fctr2
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2 prdl.descr.my.fctriPadAir#1:.clusterid.fctr2
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2 prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2 prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2 prdl.descr.my.fctriPadmini#0:.clusterid.fctr2
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2 prdl.descr.my.fctriPadmini#1:.clusterid.fctr2
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2 prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2 prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2 prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2 prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3 prdl.descr.my.fctrUnknown#0:.clusterid.fctr3
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3 prdl.descr.my.fctrUnknown#1:.clusterid.fctr3
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3 prdl.descr.my.fctriPad1#0:.clusterid.fctr3
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3 prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3 prdl.descr.my.fctriPad2#0:.clusterid.fctr3
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3 prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3 prdl.descr.my.fctriPad3#0:.clusterid.fctr3
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3 prdl.descr.my.fctriPad3#1:.clusterid.fctr3
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3 prdl.descr.my.fctriPad4#0:.clusterid.fctr3
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3 prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3 prdl.descr.my.fctriPadAir#0:.clusterid.fctr3
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3 prdl.descr.my.fctriPadAir#1:.clusterid.fctr3
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3 prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3 prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3 prdl.descr.my.fctriPadmini#0:.clusterid.fctr3
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr3 prdl.descr.my.fctriPadmini#1:.clusterid.fctr3
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3 prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3 prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3 prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3 prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4 prdl.descr.my.fctrUnknown#0:.clusterid.fctr4
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4 prdl.descr.my.fctrUnknown#1:.clusterid.fctr4
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4 prdl.descr.my.fctriPad1#0:.clusterid.fctr4
## prdl.descr.my.fctriPad1#1:.clusterid.fctr4 prdl.descr.my.fctriPad1#1:.clusterid.fctr4
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4 prdl.descr.my.fctriPad2#0:.clusterid.fctr4
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4 prdl.descr.my.fctriPad2#1:.clusterid.fctr4
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4 prdl.descr.my.fctriPad3#0:.clusterid.fctr4
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4 prdl.descr.my.fctriPad3#1:.clusterid.fctr4
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4 prdl.descr.my.fctriPad4#0:.clusterid.fctr4
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4 prdl.descr.my.fctriPad4#1:.clusterid.fctr4
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4 prdl.descr.my.fctriPadAir#0:.clusterid.fctr4
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4 prdl.descr.my.fctriPadAir#1:.clusterid.fctr4
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4 prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4 prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4 prdl.descr.my.fctriPadmini#0:.clusterid.fctr4
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4 prdl.descr.my.fctriPadmini#1:.clusterid.fctr4
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4 prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4 prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4 prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4 prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5 prdl.descr.my.fctrUnknown#0:.clusterid.fctr5
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5 prdl.descr.my.fctrUnknown#1:.clusterid.fctr5
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5 prdl.descr.my.fctriPad1#0:.clusterid.fctr5
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5 prdl.descr.my.fctriPad1#1:.clusterid.fctr5
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5 prdl.descr.my.fctriPad2#0:.clusterid.fctr5
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5 prdl.descr.my.fctriPad2#1:.clusterid.fctr5
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5 prdl.descr.my.fctriPad3#0:.clusterid.fctr5
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5 prdl.descr.my.fctriPad3#1:.clusterid.fctr5
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5 prdl.descr.my.fctriPad4#0:.clusterid.fctr5
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5 prdl.descr.my.fctriPad4#1:.clusterid.fctr5
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5 prdl.descr.my.fctriPadAir#0:.clusterid.fctr5
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5 prdl.descr.my.fctriPadAir#1:.clusterid.fctr5
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5 prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5 prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5 prdl.descr.my.fctriPadmini#0:.clusterid.fctr5
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5 prdl.descr.my.fctriPadmini#1:.clusterid.fctr5
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5 prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5 prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5 prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5 prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5
## rel.inf
## condition.fctrFor parts or not working 27.1193353
## prdl.descr.my.fctriPad1#0 9.6571442
## condition.fctrNew 9.1794635
## D.ratio.weight.sum.wrds.n 7.0025478
## prdl.descr.my.fctriPadAir2#0 6.8757587
## storage.fctr16 5.8519811
## D.ratio.wrds.stop.n.wrds.n 4.3687580
## D.weight.post.stop.sum 3.1869476
## prdl.descr.my.fctriPad1#1 2.9944047
## D.weight.post.stem.sum 2.2247274
## color.fctrWhite 2.0258970
## D.terms.post.stop.n.log 1.7071117
## storage.fctr32 1.5191710
## prdl.descr.my.fctriPadAir#0 1.5180640
## color.fctrSpace Gray 1.3189979
## D.weight.sum.stem.stop.Ratio 1.2754310
## cellular.fctrUnknown 1.2556259
## storage.fctr64 1.2301761
## D.chrs.n.log 1.1298649
## D.wrds.n.log 1.0308109
## prdl.descr.my.fctriPadmini#0 0.9454626
## cellular.fctr1 0.9018094
## color.fctrUnknown 0.8932420
## D.chrs.uppr.n.log 0.7118114
## D.chrs.pnct13.n.log 0.5560194
## cellular.fctr0:carrier.fctrNone 0.5532660
## D.wrds.unq.n.log 0.4810728
## cellular.fctr1:carrier.fctrVerizon 0.3983008
## prdl.descr.my.fctriPad2#1 0.3823508
## startprice.dcm2.is9 0.3615313
## prdl.descr.my.fctriPad2#0 0.3440625
## startprice.dgt2.is9 0.3202047
## startprice.dcm1.is9 0.2248246
## D.chrs.pnct11.n.log 0.2102228
## storage.fctrUnknown 0.1417250
## prdl.descr.my.fctriPadmini#1 0.1018754
## color.fctrGold 0.0000000
## condition.fctrManufacturer refurbished 0.0000000
## condition.fctrNew other (see details) 0.0000000
## condition.fctrSeller refurbished 0.0000000
## prdl.descr.my.fctrUnknown#1 0.0000000
## prdl.descr.my.fctriPad3#0 0.0000000
## prdl.descr.my.fctriPad3#1 0.0000000
## prdl.descr.my.fctriPad4#0 0.0000000
## prdl.descr.my.fctriPad4#1 0.0000000
## prdl.descr.my.fctriPadAir#1 0.0000000
## prdl.descr.my.fctriPadAir2#1 0.0000000
## prdl.descr.my.fctriPadmini2#0 0.0000000
## prdl.descr.my.fctriPadmini2#1 0.0000000
## prdl.descr.my.fctriPadmini3#0 0.0000000
## prdl.descr.my.fctriPadmini3#1 0.0000000
## cellular.fctr1:carrier.fctrNone 0.0000000
## cellular.fctrUnknown:carrier.fctrNone 0.0000000
## cellular.fctr0:carrier.fctrSprint 0.0000000
## cellular.fctr1:carrier.fctrSprint 0.0000000
## cellular.fctrUnknown:carrier.fctrSprint 0.0000000
## cellular.fctr0:carrier.fctrT-Mobile 0.0000000
## cellular.fctr1:carrier.fctrT-Mobile 0.0000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.0000000
## cellular.fctr0:carrier.fctrUnknown 0.0000000
## cellular.fctr1:carrier.fctrUnknown 0.0000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.0000000
## cellular.fctr0:carrier.fctrVerizon 0.0000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.0000000
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad2#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPad4#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2 0.0000000
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3 0.0000000
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad1#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4 0.0000000
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5 0.0000000
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5 0.0000000
## [1] " calling mypredict_mdl for fit:"## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.gbm
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 125 17.745 0.312
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8643368 0.2006895 0.5332267 0.1977717 0.2914982
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.09159447 0.4901601 0.0239524 0.1014019
## label step_major step_minor label_minor bgn end
## 6 fit.models_1_RFE.X 2 4 gbm 172.841 193.804
## 7 fit.models_1_RFE.X 2 5 rf 193.805 NA
## elapsed
## 6 20.963
## 7 NA
## [1] "fitting model: RFE.X.rf"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
##
## The following object is masked from 'package:ggplot2':
##
## margin
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 73 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 204 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 204 -none- numeric
## importance 144 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 204 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 144 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.rf
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 5.936 1.778
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9468689 0.1339398 0.8171931 0.232062 0.2858241
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1304232 0.7720234
## label step_major step_minor label_minor bgn end
## 7 fit.models_1_RFE.X 2 5 rf 193.805 201.58
## 8 fit.models_1_RFE.X 2 6 svmLinear 201.581 NA
## elapsed
## 7 7.775
## 8 NA
## [1] "fitting model: RFE.X.svmLinear"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## [1] "User-specified Tuning Grid Length: 5"
## C
## 1 0.01
## 2 0.05
## 3 0.10
## 4 0.50
## 5 1.00## Loading required package: kernlab
##
## Attaching package: 'kernlab'
##
## The following object is masked from 'package:ggplot2':
##
## alpha## Aggregating results
## Selecting tuning parameters
## Fitting C = 0.1 on full training set## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Length Class Mode
## 1 ksvm S4
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.svmLinear
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 2.598 0.045
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8765885 0.1297514 0.8615882 0.3199622 0.2687337
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3075978 0.7954752 0.01057618 0.03892115
## label step_major step_minor label_minor bgn end
## 8 fit.models_1_RFE.X 2 6 svmLinear 201.581 205.97
## 9 fit.models_1_RFE.X 2 7 svmLinear2 205.970 NA
## elapsed
## 8 4.389
## 9 NA
## [1] "fitting model: RFE.X.svmLinear2"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Loading required package: e1071## Aggregating results
## Selecting tuning parameters
## Fitting cost = 0.25 on full training set## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear",
## cost = param$cost, : Variable(s) 'cellular.fctr1.carrier.fctrNone'
## and 'cellular.fctrUnknown.carrier.fctrNone'
## and 'cellular.fctr0.carrier.fctrSprint' and
## 'cellular.fctrUnknown.carrier.fctrSprint'
## and 'cellular.fctr0.carrier.fctrT.Mobile' and
## 'cellular.fctrUnknown.carrier.fctrT.Mobile'
## and 'cellular.fctr0.carrier.fctrUnknown'
## and 'cellular.fctr0.carrier.fctrVerizon' and
## 'cellular.fctrUnknown.carrier.fctrVerizon' and 'prdl.descr.my.fctrUnknown.
## 0..clusterid.fctr2' and 'prdl.descr.my.fctriPad1.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPad1.1..clusterid.fctr2'
## and 'prdl.descr.my.fctriPad2.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPad3.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPad4.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadAir.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadAir.1..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadAir2.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadmini.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadmini.1..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadmini2.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadmini3.0..clusterid.fctr2'
## and 'prdl.descr.my.fctriPadmini3.1..clusterid.fctr2'
## and 'prdl.descr.my.fctrUnknown.0..clusterid.fctr3'
## and 'prdl.descr.my.fctrUnknown.1..clusterid.fctr3'
## and 'prdl.descr.my.fctriPad1.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPad1.1..clusterid.fctr3'
## and 'prdl.descr.my.fctriPad2.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPad3.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPad4.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadAir.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadAir.1..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadAir2.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadAir2.1..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadmini.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadmini2.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadmini2.1..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadmini3.0..clusterid.fctr3'
## and 'prdl.descr.my.fctriPadmini3.1..clusterid.fctr3'
## and 'prdl.descr.my.fctrUnknown.0..clusterid.fctr4'
## and 'prdl.descr.my.fctrUnknown.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad1.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad2.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad2.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad3.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad3.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad4.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPad4.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadAir.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadAir.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadAir2.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadAir2.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini2.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini2.1..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini3.0..clusterid.fctr4'
## and 'prdl.descr.my.fctriPadmini3.1..clusterid.fctr4'
## and 'prdl.descr.my.fctrUnknown.0..clusterid.fctr5'
## and 'prdl.descr.my.fctrUnknown.1..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad1.0..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad1.1..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad2.0..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad2.1..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad3.0..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad3.1..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad4.0..clusterid.fctr5'
## and 'prdl.descr.my.fctriPad4.1..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadAir.0..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadAir.1..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadAir2.0..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadAir2.1..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini.0..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini.1..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini2.0..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini2.1..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini3.0..clusterid.fctr5' and
## 'prdl.descr.my.fctriPadmini3.1..clusterid.fctr5' constant. Cannot scale
## data.
##
## Call:
## svm.default(x = as.matrix(x), y = y, kernel = "linear", cost = param$cost)
##
##
## Parameters:
## SVM-Type: eps-regression
## SVM-Kernel: linear
## cost: 0.25
## gamma: 0.006944444
## epsilon: 0.1
##
##
## Number of Support Vectors: 71
##
##
##
##
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.svmLinear2
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.878 0.044
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8834809 0.127848 0.8693183 0.3251708 0.2676586
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3129012 0.7947689 0.009445519 0.03760688
## label step_major step_minor label_minor bgn end
## 9 fit.models_1_RFE.X 2 7 svmLinear2 205.970 209.731
## 10 fit.models_1_RFE.X 2 8 earth 209.731 NA
## elapsed
## 9 3.761
## 10 NA
## [1] "fitting model: RFE.X.earth"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Loading required package: earth
## Loading required package: plotmo
## Loading required package: plotrix
## Loading required package: TeachingDemos
## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 9, degree = 1 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[204,144], y=c(2.301,2.301,1...), keepxy=TRUE,
## degree=1, nprune=9)
##
## coefficients
## (Intercept) 2.3949624
## condition.fctrFor parts or not working -0.3809226
## condition.fctrNew 0.2167796
## prdl.descr.my.fctriPad1#0 -0.3625211
## prdl.descr.my.fctriPad1#1 -0.3281338
## prdl.descr.my.fctriPadAir#0 0.1807382
## prdl.descr.my.fctriPadAir2#0 0.2513881
## prdl.descr.my.fctriPadAir2#1 0.2807885
## storage.fctr16 -0.1438518
##
## Selected 9 of 42 terms, and 8 of 144 predictors
## Termination condition: RSq changed by less than 0.001 at 42 terms
## Importance: condition.fctrForpartsornotworking, ...
## Number of terms at each degree of interaction: 1 8 (additive model)
## GCV 0.01778728 RSS 3.049037 GRSq 0.776173 RSq 0.8100656
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.earth
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 3.675 0.24
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8100656 0.1334478 0.346497 0.2662697 0.2786261
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1691583 0.7737232 0.01402102 0.06316611
## label step_major step_minor label_minor bgn end
## 10 fit.models_1_RFE.X 2 8 earth 209.731 215.889
## 11 fit.models_1_RFE.X 2 9 bagEarth 215.889 NA
## elapsed
## 10 6.158
## 11 NA
## [1] "fitting model: RFE.X.bagEarth"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## [1] "User-specified Tuning Grid Length: 5"
## NULL
## + : degree=1, nprune=33
## - : degree=1, nprune=33
## Aggregating results
## Fitting final model on full training set
##
## Call:
## bagEarth.default(x = structure(c(0, 4.53259949315326, 0, 4.59511985013459, 0, 2.83321334405622, 4.27666611901606, 4.45434729625351, 4.58496747867057, 3.04452243772342, 0, 4.59511985013459, 4.45434729625351, 0, 4.55387689160054, 4.53259949315326, 0, 4.53259949315326, 4.00733318523247, 3.52636052461616, 4.60517018598809, 0, 0, 0, 0, 3.80666248977032, 0, 0, 4.63472898822964, 4.36944785246702, 4.40671924726425, 0, 0, 3.73766961828337, 0, 0, 0, 0, 0, 0, 0, 3.29583686600433, 0, 2.19722457733622, 0, 0, 0, 4.63472898822964, 4.56434819146784, 0, 0, 0, 0, 3.36729582998647, 0, 0, 3.55534806148941, 3.2188758248682, 4.31748811353631, 3.98898404656427, 0, 0, 0, 0, 3.8286413964891, 4.54329478227, 0, 2.56494935746154, 0, 4.61512051684126, 0, 2.70805020110221, 0, 0, 0, 0, 0, 3.87120101090789, 4.60517018598809, 4.34380542185368, 0, 4.62497281328427, 4.56434819146784, 4.62497281328427, 4.57471097850338, 0, 0, 0, 0, 0, 0, 4.33073334028633, 0, 0, 4.40671924726425, 0, 4.62497281328427, 0, 0, 0, 0, 4.07753744390572, 0, 0, 4.52178857704904, 4.60517018598809, 2.99573227355399, 0, 4.59511985013459, 0, 0, 0, 4.02535169073515, 4.63472898822964, 0, 0, 0, 0, 3.63758615972639, 0, 3.55534806148941, 0, 0, 4.27666611901606, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.99573227355399, 0, 0, 0, 0, 0, 3.66356164612965, 4.64439089914137, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.56434819146784, 0, 0, 0, 0, 4.57471097850338, 0, 0, 0, 1.94591014905531, 0, 0, 0, 4.26267987704132, 0, 0, 4.02535169073515, 4.60517018598809, 0, 4.02535169073515, 0, 4.26267987704132, 4.00733318523247, 4.02535169073515, 0, 4.31748811353631, 4.26267987704132, 4.61512051684126, 4.58496747867057, 0, 3.98898404656427, 4.58496747867057, 0, 4.58496747867057, 0, 4.58496747867057, 4.26267987704132, 4.60517018598809, 4.60517018598809, 4.57471097850338, 4.60517018598809, 4.57471097850338, 4.53259949315326, 0, 0, 4.55387689160054, 4.62497281328427, 4.58496747867057, 4.53259949315326, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 1.38629436111989, 0, 1.38629436111989, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 1.09861228866811, 0, 1.09861228866811, 0, 0.693147180559945, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0, 1.38629436111989, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0, 0, 0, 1.94591014905531, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 1.09861228866811, 0, 0.693147180559945, 1.09861228866811, 0.693147180559945, 0.693147180559945, 0.693147180559945, 0, 0, 0.693147180559945, 0, 1.09861228866811, 0.693147180559945, 0, 0.693147180559945, 1.09861228866811, 0, 1.09861228866811, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 1.38629436111989, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 1.09861228866811, 1.09861228866811, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0.693147180559945, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 1.09861228866811, 0, 0.693147180559945, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0.693147180559945, 1.09861228866811, 0, 0, 1.09861228866811, 0, 0, 0, 0.693147180559945, 0.693147180559945, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 1.09861228866811, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0, 0, 0, 0.693147180559945, 0, 0, 0.693147180559945, 0, 0.693147180559945, 1.38629436111989, 0, 1.38629436111989, 0.693147180559945, 0, 0.693147180559945, 0, 0.693147180559945, 0, 0, 0, 0.693147180559945, 0, 0.693147180559945, 0.693147180559945, 0, 0, 1.09861228866811, 1.38629436111989, 1.38629436111989, 0.693147180559945, 0, 4.33073334028633, 0, 4.38202663467388, 0, 2.63905732961526, 4.04305126783455, 4.21950770517611, 4.31748811353631, 2.83321334405622, 0, 4.36944785246702, 4.21950770517611, 0, 4.30406509320417, 4.26267987704132, 0, 4.26267987704132, 3.8286413964891, 3.3322045101752, 4.38202663467388, 0, 0, 0, 0, 3.61091791264422, 0, 0, 4.44265125649032, 4.0943445622221, 4.21950770517611, 0, 0, 3.55534806148941, 0, 0, 0, 0, 0, 0, 0, 2.99573227355399, 0, 2.07944154167984, 0, 0, 0, 4.39444915467244, 4.35670882668959, 0, 0, 0, 0, 3.25809653802148, 0, 0, 3.17805383034795, 2.83321334405622, 4.07753744390572, 3.78418963391826, 0, 0, 0, 0, 3.63758615972639, 4.34380542185368, 0, 2.39789527279837, 0, 4.39444915467244, 0, 2.63905732961526, 0, 0, 0, 0, 0, 3.63758615972639, 4.38202663467388, 4.11087386417331, 0, 4.38202663467388, 4.33073334028633, 4.35670882668959, 4.35670882668959, 0, 0, 0, 0, 0, 0, 4.0943445622221, 0, 0, 4.12713438504509, 0, 4.40671924726425, 0, 0, 0, 0, 3.89182029811063, 0, 0, 4.26267987704132, 4.33073334028633, 2.94443897916644, 0, 4.38202663467388, 0, 0, 0, 3.85014760171006, 4.38202663467388, 0, 0, 0, 0, 3.43398720448515, 0, 3.29583686600433, 0, 0, 4.06044301054642, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.70805020110221, 0, 0, 0, 0, 0, 3.43398720448515, 4.26267987704132, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.33073334028633, 0, 0, 0, 0, 4.33073334028633, 0, 0, 0, 1.94591014905531, 0, 0, 0, 4.02535169073515, 0, 0, 3.87120101090789, 4.4188406077966, 0, 3.87120101090789, 0, 4.02535169073515, 3.8286413964891, 3.87120101090789, 0, 4.12713438504509, 4.02535169073515, 4.38202663467388, 4.35670882668959, 0, 3.73766961828337, 4.36944785246702, 0, 4.36944785246702, 0, 4.31748811353631, 4.02535169073515, 4.38202663467388, 4.38202663467388, 4.33073334028633, 4.38202663467388, 4.33073334028633, 4.27666611901606, 0, 0, 4.33073334028633, 4.40671924726425, 4.36944785246702, 4.30406509320417, 0, 0.288933061526546, 0, 0.400620361107501, 0, 1.44451265187586, 0.50299453901912, 0.279101906166653, 0.236247138628818, 1.49898084448892, 0, 0.266650469752977, 0.279101906166653, 0, 0.34995523470034, 0.270005482272871, 0, 0.270005482272871, 0.619153899101133, 0.892010077249937, 0.225834996183739, 0, 0, 0, 0, 0.784753155293398, 0, 0, 0.385692173823627, 0.418604327668974, 0.43844719112261, 0, 0, 0.879097804691422, 0, 0, 0, 0, 0, 0, 0, 0.695912949317636, 0, 2.17978066475618, 0, 0, 0, 0.264644326392421, 0.405567087569804, 0, 0, 0, 0, 0.829142988598458, 0, 0, 0.81298853162187, 0.731859707125995, 0.413934617415974, 0.609835280423088, 0, 0, 0, 0, 0.56122237566187, 0.356457935955424, 0, 1.60920298296807, 0, 0.228510213865094, 0, 1.39182589863527, 0, 0, 0, 0, 0, 0.739117250233987, 0.225834996183739, 0.42926863663941, 0, 0.222817739695076, 0.364350486647451, 0.203028534151002, 0.348228352193509, 0, 0, 0, 0, 0, 0, 0.477529742637923, 0, 0, 0.337359448645342, 0, 0.221580794064804, 0, 0, 0, 0, 0.553991032434917, 0, 0, 0.441383321197024, 0.252653690394677, 1.71543773825158, 0, 0.358861001246086, 0, 0, 0, 0.789604744978881, 0.203474274397906, 0, 0, 0, 0, 0.565922595960178, 0, 0.514660818362525, 0, 0, 0.490207957474472, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.68098416494939, 0, 0, 0, 0, 0, 0.794491222809742, 0.269712247155834, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25065466192721, 0, 0, 0, 0, 0.35197005221631, 0, 0, 0, 6.78840462831624, 0, 0, 0, 0.540871677252777, 0, 0, 0.764260050031879, 0.501355913080734, 0, 0.764260050031879, 0, 0.540871677252777, 0.87020526087249, 0.764260050031879, 0, 0.444760958974031, 0.540871677252777, 0.327560738909287, 0.410886265952023, 0, 0.692674517798951, 0.367393014362173, 0, 0.370202760813276, 0, 0.210308104611217, 0.540871677252777, 0.226594009432547, 0.226594009432547, 0.190105660644777, 0.226594009432547, 0.190105660644777, 0.225795803098765, 0, 0, 0.376792404273518, 0.242452682646383, 0.329062002628398, 0.306281585663577, 1, 0.3125, 1, 0.294117647058823, 1, 0.5, 0.357142857142857, 0.529411764705882, 0.590909090909091, 0.4, 1, 0.35, 0.529411764705882, 1, 0.352941176470588, 0.5, 1, 0.5, 0.333333333333333, 0.285714285714286, 0.578947368421053, 1, 1, 1, 1, 0.444444444444444, 1, 1, 0.0769230769230769, 0.533333333333333, 0.428571428571429, 1, 1, 0.375, 1, 1, 1, 1, 1, 1, 1, 0.6, 1, 0.333333333333333, 1, 1, 1, 0.619047619047619, 0.1875, 1, 1, 1, 1, 0.4, 1, 1, 0.142857142857143, 0.571428571428571, 0.230769230769231, 0.5, 1, 1, 1, 1, 0.444444444444444, 0.411764705882353, 1, 0.25, 1, 0.55, 1, 0.333333333333333, 1, 1, 1, 1, 1, 0.3, 0.578947368421053, 0.285714285714286, 1, 0.666666666666667, 0.3125, 0.695652173913043, 0.411764705882353, 1, 1, 1, 1, 1, 1, 0.533333333333333, 1, 1, 0.375, 1, 0.5, 1, 1, 1, 1, 0.111111111111111, 1, 1, 0.25, 0.333333333333333, 0.333333333333333, 1, 0.411764705882353, 1, 1, 1, 0.6, 0.571428571428571, 1, 1, 1, 1, 0.625, 1, 0.285714285714286, 1, 1, 0.307692307692308, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.25, 1, 1, 1, 1, 1, 0.666666666666667, 0.421052631578947, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.388888888888889, 1, 1, 1, 1, 0.125, 1, 1, 1, 0.5, 1, 1, 1, 0.5, 1, 1, 0.4, 0.142857142857143, 1, 0.4, 1, 0.5, 0.222222222222222, 0.4, 1, 0.307692307692308, 0.5, 0.666666666666667, 0.375, 1, 0.4, 0.526315789473684, 1, 0.3125, 1, 0.421052631578947, 0.5, 0.55, 0.55, 0.619047619047619, 0.55, 0.619047619047619, 0.6, 1, 1, 0.5, 0.333333333333333, 0.375, 0.555555555555556, 0, 2.484906649788, 0, 2.56494935746154, 0, 1.09861228866811, 2.30258509299405, 2.07944154167984, 2.19722457733622, 1.09861228866811, 0, 2.19722457733622, 2.07944154167984, 0, 2.39789527279837, 2.07944154167984, 0, 2.07944154167984, 1.94591014905531, 1.6094379124341, 2.19722457733622, 0, 0, 0, 0, 1.79175946922805, 0, 0, 2.30258509299405, 1.94591014905531, 2.19722457733622, 0, 0, 1.79175946922805, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 1.09861228866811, 0, 0, 0, 1.94591014905531, 2.484906649788, 0, 0, 0, 0, 1.38629436111989, 0, 0, 1.79175946922805, 1.09861228866811, 2.19722457733622, 1.79175946922805, 0, 0, 0, 0, 1.38629436111989, 2.30258509299405, 0, 1.38629436111989, 0, 2.30258509299405, 0, 1.09861228866811, 0, 0, 0, 0, 0, 1.79175946922805, 2.19722457733622, 2.30258509299405, 0, 1.6094379124341, 2.30258509299405, 2.07944154167984, 2.39789527279837, 0, 0, 0, 0, 0, 0, 1.6094379124341, 0, 0, 2.30258509299405, 0, 2.30258509299405, 0, 0, 0, 0, 2.07944154167984, 0, 0, 2.39789527279837, 2.484906649788, 1.09861228866811, 0, 2.30258509299405, 0, 0, 0, 1.38629436111989, 2.07944154167984, 0, 0, 0, 0, 1.38629436111989, 0, 0.693147180559945, 0, 0, 2.07944154167984, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0, 0, 0, 0, 1.38629436111989, 2.19722457733622, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.484906649788, 0, 0, 0, 0, 2.484906649788, 0, 0, 0, 0.693147180559945, 0, 0, 0, 2.07944154167984, 0, 0, 1.94591014905531, 2.30258509299405, 0, 1.94591014905531, 0, 2.07944154167984, 1.94591014905531, 1.94591014905531, 0, 2.19722457733622, 2.07944154167984, 1.6094379124341, 2.19722457733622, 0, 1.79175946922805, 1.6094379124341, 0, 2.39789527279837, 0, 2.19722457733622, 2.07944154167984, 2.30258509299405, 2.30258509299405, 2.19722457733622, 2.30258509299405, 2.19722457733622, 2.19722457733622, 0, 0, 2.30258509299405, 1.94591014905531, 2.30258509299405, 2.07944154167984, 0, 4.33399592289818, 0, 6.40992577772002, 0, 4.33353795562757, 6.53892900724855, 4.46563049866644, 4.96118991120518, 5.99592337795567, 0, 5.06635892530656, 4.46563049866644, 0, 5.59928375520544, 4.5900931986388, 0, 4.5900931986388, 4.95323119280907, 5.35206046349962, 4.0650299313073, 0, 0, 0, 0, 6.27802524234719, 0, 0, 4.62830608588352, 5.86046058736564, 5.69981348459393, 0, 0, 6.15368463283995, 0, 0, 0, 0, 0, 0, 0, 2.78365179727054, 0, 4.35956132951237, 0, 0, 0, 5.29288652784842, 6.08350631354705, 0, 0, 0, 0, 3.31657195439383, 0, 0, 4.87793118973122, 4.39115824275597, 4.96721540899169, 5.48851752380779, 0, 0, 0, 0, 4.48977900529496, 5.70332697528678, 0, 4.82760894890422, 0, 4.34169406343679, 0, 2.78365179727054, 0, 0, 0, 0, 0, 6.65205525210589, 4.0650299313073, 5.58049227631232, 0, 4.45635479390152, 5.46525729971176, 4.46662775132205, 5.57165363509615, 0, 0, 0, 0, 0, 0, 6.68541639693092, 0, 0, 5.06039172968012, 0, 4.21003508723128, 0, 0, 0, 0, 4.43192825947934, 0, 0, 6.62074981795536, 5.05307380789355, 3.43087547650317, 0, 5.74177601993737, 0, 0, 0, 7.10644270480993, 4.06948548795812, 0, 0, 0, 0, 3.96145817172125, 0, 3.08796491017515, 0, 0, 5.88249548969367, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.04295249484818, 0, 0, 0, 0, 0, 6.35592978247794, 4.85482044880501, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.26112925276257, 0, 0, 0, 0, 5.27955078324464, 0, 0, 0, 6.78840462831624, 0, 0, 0, 7.0313318042861, 0, 0, 6.87834045028692, 6.51762687004954, 0, 6.87834045028692, 0, 7.0313318042861, 6.96164208697992, 6.87834045028692, 0, 5.33713150768837, 7.0313318042861, 6.55121477818575, 6.16329398928035, 0, 6.23407066019056, 6.6130742585191, 0, 5.55304141219913, 0, 3.78554588300191, 7.0313318042861, 4.30528617921838, 4.30528617921838, 3.80211321289555, 4.30528617921838, 3.80211321289555, 4.29012025887654, 0, 0, 6.4054708726498, 4.1216956049885, 4.93593003942596, 5.20678695628081, 0, 4.74469402141615, 0, 6.50155375051593, 0, 5.33353795562757, 6.55643359358039, 4.81778260859203, 5.73453424007135, 6.12658741989736, 0, 5.29970060894862, 4.81778260859203, 0, 6.05421622623684, 5.37989687851308, 0, 5.37989687851308, 4.96982047173422, 6.77318500706764, 4.09822313443103, 0, 0, 0, 0, 7.2149248772016, 0, 0, 4.81156203147534, 5.86046058736564, 5.69981348459393, 0, 0, 6.17359176755014, 0, 0, 0, 0, 0, 0, 0, 2.78365179727054, 0, 4.35956132951237, 0, 0, 0, 5.29288652784842, 6.18611677572259, 0, 0, 0, 0, 3.40368131568829, 0, 0, 4.87793118973122, 4.39115824275597, 5.10126550648807, 5.48851752380779, 0, 0, 0, 0, 4.48977900529496, 5.81443808639789, 0, 4.82760894890422, 0, 4.43708601610869, 0, 2.78365179727054, 0, 0, 0, 0, 0, 6.85205525210589, 4.09822313443103, 5.58049227631232, 0, 4.64610276902557, 5.99288268989974, 4.78425064184838, 6.02677679943701, 0, 0, 0, 0, 0, 0, 6.68541639693092, 0, 0, 5.0677917878245, 0, 4.41097331680289, 0, 0, 0, 0, 5.07785105353862, 0, 0, 6.62074981795536, 5.6044776619847, 3.43087547650317, 0, 5.75283553922081, 0, 0, 0, 7.10644270480993, 4.46485295546078, 0, 0, 0, 0, 3.96145817172125, 0, 3.34929299405854, 0, 0, 5.89671487162951, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5.04295249484818, 0, 0, 0, 0, 0, 6.38910834032824, 5.34620665224309, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.66312700473873, 0, 0, 0, 0, 5.46483080271834, 0, 0, 0, 6.78840462831624, 0, 0, 0, 7.0313318042861, 0, 0, 6.87834045028692, 6.80304523373769, 0, 6.87834045028692, 0, 7.0313318042861, 6.96164208697992, 6.87834045028692, 0, 5.50906889566371, 7.0313318042861, 6.55121477818575, 6.27800748680173, 0, 6.60741735401787, 6.6130742585191, 0, 5.68046918132823, 0, 3.86585597221495, 7.0313318042861, 4.3699472882484, 4.3699472882484, 4.05992775464868, 4.3699472882484, 4.05992775464868, 4.3485926290454, 0, 0, 6.60546813839336, 4.15436161547393, 5.32530612692285, 5.23167087466854, 1, 0.913440551347633, 1, 0.985906757628722, 1, 0.812507193476542, 0.997330166456811, 0.926905770032554, 0.865142608537891, 0.978672622622288, 1, 0.95597078007613, 0.926905770032554, 1, 0.924856917224085, 0.85319352810856, 1, 0.85319352810856, 0.996661996339807, 0.790183710900394, 0.991900586660385, 1, 1, 1, 1, 0.870144228692537, 1, 1, 0.961913419302706, 1, 1, 1, 1, 0.996775437142634, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.983412782865944, 1, 1, 1, 1, 0.974407309846266, 1, 1, 1, 1, 0.973722187695212, 1, 1, 1, 1, 1, 1, 0.980890481683683, 1, 1, 1, 0.978501216265453, 1, 1, 1, 1, 1, 1, 1, 0.970811677279086, 0.991900586660385, 1, 1, 0.959159755055559, 0.911957997930241, 0.933610733570676, 0.924483155841547, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.998539786468308, 1, 0.954445829720582, 1, 1, 1, 1, 0.872796033745583, 1, 1, 1, 0.901613694023381, 1, 1, 0.998077553372066, 1, 1, 1, 1, 0.911448938756402, 1, 1, 1, 1, 1, 1, 0.921975149875818, 1, 1, 0.997588592590044, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.994807012796937, 0.908086941751137, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.91379223607514, 1, 1, 1, 1, 0.966095927547924, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.95804549964291, 1, 1, 1, 1, 1, 1, 1, 0.968790118397199, 1, 1, 0.981727722089765, 1, 0.943495820859525, 1, 1, 0.977567386590538, 1, 0.979225793772388, 1, 0.98520322906322, 0.98520322906322, 0.936497751355814, 0.98520322906322, 0.936497751355814, 0.986553725502292, 1, 1, 0.969722469088739, 0.992136936186838, 0.926881933504566, 0.995243600183602, 0, 2.77258872223978, 0, 2.83321334405622, 0, 1.38629436111989, 2.63905732961526, 2.83321334405622, 3.09104245335832, 1.6094379124341, 0, 2.99573227355399, 2.83321334405622, 0, 2.83321334405622, 2.89037175789616, 0, 2.89037175789616, 2.19722457733622, 1.94591014905531, 2.94443897916644, 0, 0, 0, 0, 2.19722457733622, 0, 0, 2.56494935746154, 2.70805020110221, 2.63905732961526, 0, 0, 2.07944154167984, 0, 0, 0, 0, 0, 0, 0, 1.6094379124341, 0, 1.09861228866811, 0, 0, 0, 3.04452243772342, 2.77258872223978, 0, 0, 0, 0, 1.6094379124341, 0, 0, 1.94591014905531, 1.94591014905531, 2.56494935746154, 2.30258509299405, 0, 0, 0, 0, 2.19722457733622, 2.83321334405622, 0, 1.38629436111989, 0, 2.99573227355399, 0, 1.09861228866811, 0, 0, 0, 0, 0, 2.30258509299405, 2.94443897916644, 2.63905732961526, 0, 3.04452243772342, 2.77258872223978, 3.13549421592915, 2.83321334405622, 0, 0, 0, 0, 0, 0, 2.70805020110221, 0, 0, 2.77258872223978, 0, 2.99573227355399, 0, 0, 0, 0, 2.19722457733622, 0, 0, 2.77258872223978, 3.04452243772342, 1.09861228866811, 0, 2.83321334405622, 0, 0, 0, 2.30258509299405, 3.04452243772342, 0, 0, 0, 0, 2.07944154167984, 0, 1.94591014905531, 0, 0, 2.56494935746154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.38629436111989, 0, 0, 0, 0, 0, 2.19722457733622, 2.94443897916644, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.89037175789616, 0, 0, 0, 0, 2.77258872223978, 0, 0, 0, 0.693147180559945, 0, 0, 0, 2.63905732961526, 0, 0, 2.30258509299405, 2.63905732961526, 0, 2.30258509299405, 0, 2.63905732961526, 2.19722457733622, 2.30258509299405, 0, 2.56494935746154, 2.63905732961526, 3.04452243772342, 2.77258872223978, 0, 2.30258509299405, 2.94443897916644, 0, 2.77258872223978, 0, 2.94443897916644, 2.63905732961526, 2.99573227355399, 2.99573227355399, 3.04452243772342, 2.99573227355399, 3.04452243772342, 2.99573227355399, 0, 0, 2.89037175789616, 2.89037175789616, 2.77258872223978, 2.89037175789616, 0, 2.484906649788, 0, 2.56494935746154, 0, 1.09861228866811, 2.30258509299405, 1.94591014905531, 2.19722457733622, 1.09861228866811, 0, 2.19722457733622, 1.94591014905531, 0, 2.39789527279837, 2.07944154167984, 0, 2.07944154167984, 1.94591014905531, 1.6094379124341, 2.19722457733622, 0, 0, 0, 0, 1.79175946922805, 0, 0, 2.30258509299405, 1.94591014905531, 2.19722457733622, 0, 0, 1.79175946922805, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 1.09861228866811, 0, 0, 0, 1.94591014905531, 2.484906649788, 0, 0, 0, 0, 1.38629436111989, 0, 0, 1.79175946922805, 1.09861228866811, 2.19722457733622, 1.79175946922805, 0, 0, 0, 0, 1.38629436111989, 2.30258509299405, 0, 1.38629436111989, 0, 2.30258509299405, 0, 1.09861228866811, 0, 0, 0, 0, 0, 1.79175946922805, 2.19722457733622, 2.30258509299405, 0, 1.6094379124341, 2.30258509299405, 2.07944154167984, 2.39789527279837, 0, 0, 0, 0, 0, 0, 1.6094379124341, 0, 0, 2.30258509299405, 0, 2.30258509299405, 0, 0, 0, 0, 2.07944154167984, 0, 0, 2.39789527279837, 2.484906649788, 1.09861228866811, 0, 2.30258509299405, 0, 0, 0, 1.38629436111989, 1.94591014905531, 0, 0, 0, 0, 1.38629436111989, 0, 0.693147180559945, 0, 0, 2.07944154167984, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.09861228866811, 0, 0, 0, 0, 0, 1.38629436111989, 2.19722457733622, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.484906649788, 0, 0, 0, 0, 2.484906649788, 0, 0, 0, 0.693147180559945, 0, 0, 0, 2.07944154167984, 0, 0, 1.94591014905531, 2.30258509299405, 0, 1.94591014905531, 0, 2.07944154167984, 1.94591014905531, 1.94591014905531, 0, 2.19722457733622, 2.07944154167984, 1.6094379124341, 2.19722457733622, 0, 1.79175946922805, 1.6094379124341, 0, 2.39789527279837, 0, 2.19722457733622, 2.07944154167984, 2.30258509299405, 2.30258509299405, 2.07944154167984, 2.30258509299405, 2.07944154167984, 2.19722457733622, 0, 0, 2.30258509299405, 1.94591014905531, 2.30258509299405, 2.07944154167984, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(204L, 144L), .Dimnames = list(c("2", "6", "8", "9", "13", "15", "24", "25", "26", "30", "34", "38", "41", "42", "55", "59", "62", "67", "70", "74", "76", "79", "82", "87", "93", "100", "106", "108", "116", "117", "124", "131", "137", "142", "146", "150", "152", "156", "158", "162", "165", "168", "169", "170", "174", "177", "179", "184", "194", "197", "198", "204", "207", "216", "221", "222", "225", "231", "234", "251", "252", "253", "257", "259", "273", "283", "284", "286", "288", "293", "297", "307", "308", "323", "331", "335", "340", "351", "352", "355", "361", "368", "369", "378", "379", "389", "391", "397", "402", "404", "408", "418", "422", "430", "438", "445", "446", "452", "462", "466", "468", "469", "486", "494", "498", "508", "512", "515", "534", "541", "547", "557", "561", "566", "569", "581", "584", "585", "590", "598", "628", "629", "636", "654", "673", "675", "680", "697", "713", "727", "728", "733", "767", "775", "777", "778", "779", "781", "782", "783", "784", "785", "786", "787", "789", "790", "792", "794", "796", "797", "798", "800", "801", "806", "811", "812", "816", "820", "831", "837", "845", "857", "864", "865", "870", "874", "876", "878", "879", "889", "893", "897", "906", "908", "909", "911", "912", "914", "915", "917", "919", "920", "922", "931", "939", "951", "953", "965", "967", "968", "970", "972", "974", "976", "977", "980", "981", "983", "989", "996", "1006", "1009", "1011", "1014"), c("D.chrs.n.log", "D.chrs.pnct11.n.log", "D.chrs.pnct13.n.log", "D.chrs.uppr.n.log", "D.ratio.weight.sum.wrds.n", "D.ratio.wrds.stop.n.wrds.n", "D.terms.post.stop.n.log", "D.weight.post.stem.sum", "D.weight.post.stop.sum", "D.weight.sum.stem.stop.Ratio", "D.wrds.n.log", "D.wrds.unq.n.log", "cellular.fctr1", "cellular.fctrUnknown", "color.fctrGold", "color.fctrSpace Gray", "color.fctrUnknown", "color.fctrWhite", "condition.fctrFor parts or not working", "condition.fctrManufacturer refurbished", "condition.fctrNew", "condition.fctrNew other (see details)", "condition.fctrSeller refurbished", "prdl.descr.my.fctrUnknown#1", "prdl.descr.my.fctriPad1#0", "prdl.descr.my.fctriPad1#1", "prdl.descr.my.fctriPad2#0", "prdl.descr.my.fctriPad2#1", "prdl.descr.my.fctriPad3#0", "prdl.descr.my.fctriPad3#1", "prdl.descr.my.fctriPad4#0", "prdl.descr.my.fctriPad4#1", "prdl.descr.my.fctriPadAir#0", "prdl.descr.my.fctriPadAir#1", "prdl.descr.my.fctriPadAir2#0", "prdl.descr.my.fctriPadAir2#1", "prdl.descr.my.fctriPadmini#0", "prdl.descr.my.fctriPadmini#1", "prdl.descr.my.fctriPadmini2#0", "prdl.descr.my.fctriPadmini2#1", "prdl.descr.my.fctriPadmini3#0", "prdl.descr.my.fctriPadmini3#1", "startprice.dcm1.is9", "startprice.dcm2.is9", "startprice.dgt2.is9", "storage.fctr16", "storage.fctr32", "storage.fctr64", "storage.fctrUnknown", "cellular.fctr0:carrier.fctrNone", "cellular.fctr1:carrier.fctrNone", "cellular.fctrUnknown:carrier.fctrNone", "cellular.fctr0:carrier.fctrSprint", "cellular.fctr1:carrier.fctrSprint", "cellular.fctrUnknown:carrier.fctrSprint", "cellular.fctr0:carrier.fctrT-Mobile", "cellular.fctr1:carrier.fctrT-Mobile", "cellular.fctrUnknown:carrier.fctrT-Mobile", "cellular.fctr0:carrier.fctrUnknown", "cellular.fctr1:carrier.fctrUnknown", "cellular.fctrUnknown:carrier.fctrUnknown", "cellular.fctr0:carrier.fctrVerizon", "cellular.fctr1:carrier.fctrVerizon", "cellular.fctrUnknown:carrier.fctrVerizon", "prdl.descr.my.fctrUnknown#0:.clusterid.fctr2", "prdl.descr.my.fctrUnknown#1:.clusterid.fctr2", "prdl.descr.my.fctriPad1#0:.clusterid.fctr2", "prdl.descr.my.fctriPad1#1:.clusterid.fctr2", "prdl.descr.my.fctriPad2#0:.clusterid.fctr2", "prdl.descr.my.fctriPad2#1:.clusterid.fctr2", "prdl.descr.my.fctriPad3#0:.clusterid.fctr2", "prdl.descr.my.fctriPad3#1:.clusterid.fctr2", "prdl.descr.my.fctriPad4#0:.clusterid.fctr2", "prdl.descr.my.fctriPad4#1:.clusterid.fctr2", "prdl.descr.my.fctriPadAir#0:.clusterid.fctr2", "prdl.descr.my.fctriPadAir#1:.clusterid.fctr2", "prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2", "prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2", "prdl.descr.my.fctriPadmini#0:.clusterid.fctr2", "prdl.descr.my.fctriPadmini#1:.clusterid.fctr2", "prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2", "prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2", "prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2", "prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2", "prdl.descr.my.fctrUnknown#0:.clusterid.fctr3", "prdl.descr.my.fctrUnknown#1:.clusterid.fctr3", "prdl.descr.my.fctriPad1#0:.clusterid.fctr3", "prdl.descr.my.fctriPad1#1:.clusterid.fctr3", "prdl.descr.my.fctriPad2#0:.clusterid.fctr3", "prdl.descr.my.fctriPad2#1:.clusterid.fctr3", "prdl.descr.my.fctriPad3#0:.clusterid.fctr3", "prdl.descr.my.fctriPad3#1:.clusterid.fctr3", "prdl.descr.my.fctriPad4#0:.clusterid.fctr3", "prdl.descr.my.fctriPad4#1:.clusterid.fctr3", "prdl.descr.my.fctriPadAir#0:.clusterid.fctr3", "prdl.descr.my.fctriPadAir#1:.clusterid.fctr3", "prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3", "prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3", "prdl.descr.my.fctriPadmini#0:.clusterid.fctr3", "prdl.descr.my.fctriPadmini#1:.clusterid.fctr3", "prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3", "prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3", "prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3", "prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3", "prdl.descr.my.fctrUnknown#0:.clusterid.fctr4", "prdl.descr.my.fctrUnknown#1:.clusterid.fctr4", "prdl.descr.my.fctriPad1#0:.clusterid.fctr4", "prdl.descr.my.fctriPad1#1:.clusterid.fctr4", "prdl.descr.my.fctriPad2#0:.clusterid.fctr4", "prdl.descr.my.fctriPad2#1:.clusterid.fctr4", "prdl.descr.my.fctriPad3#0:.clusterid.fctr4", "prdl.descr.my.fctriPad3#1:.clusterid.fctr4", "prdl.descr.my.fctriPad4#0:.clusterid.fctr4", "prdl.descr.my.fctriPad4#1:.clusterid.fctr4", "prdl.descr.my.fctriPadAir#0:.clusterid.fctr4", "prdl.descr.my.fctriPadAir#1:.clusterid.fctr4", "prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4", "prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4", "prdl.descr.my.fctriPadmini#0:.clusterid.fctr4", "prdl.descr.my.fctriPadmini#1:.clusterid.fctr4", "prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4", "prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4", "prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4", "prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4", "prdl.descr.my.fctrUnknown#0:.clusterid.fctr5", "prdl.descr.my.fctrUnknown#1:.clusterid.fctr5", "prdl.descr.my.fctriPad1#0:.clusterid.fctr5", "prdl.descr.my.fctriPad1#1:.clusterid.fctr5", "prdl.descr.my.fctriPad2#0:.clusterid.fctr5", "prdl.descr.my.fctriPad2#1:.clusterid.fctr5", "prdl.descr.my.fctriPad3#0:.clusterid.fctr5", "prdl.descr.my.fctriPad3#1:.clusterid.fctr5", "prdl.descr.my.fctriPad4#0:.clusterid.fctr5", "prdl.descr.my.fctriPad4#1:.clusterid.fctr5", "prdl.descr.my.fctriPadAir#0:.clusterid.fctr5", "prdl.descr.my.fctriPadAir#1:.clusterid.fctr5", "prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5", "prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5", "prdl.descr.my.fctriPadmini#0:.clusterid.fctr5", "prdl.descr.my.fctriPadmini#1:.clusterid.fctr5", "prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5", "prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5", "prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5", "prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5"))), y = structure(c(2.301008280397, 2.301008280397, 1.8750612633917, 2.301008280397, 2.57403126772772, 2.09691001300806, 2.50508211450511, 2.26717172840301, 1.95424250943932, 2.35218251811136, 2.301008280397, 2.301008280397, 2.26717172840301, 2.78886805401911, 1.9030356967885, 2.11394335230684, 2.57978359661681, 2.11394335230684, 2.17606230512508, 2.13017288889254, 2.42322948515657, 2.29885307640971, 2.53146614348729, 2.33243845991561, 2.17609125905568, 2.27873074274698, 2.29885307640971, 2.69810054562339, 2.26705433565141, 2.50379068305718, 2.61804809671209, 2.24303804868629, 2.53019969820308, 1.95419425181586, 2.17609125905568, 2.48429983934679, 2.47712125471966, 1.90308998699194, 2.48429983934679, 2.53781909507327, 1.90308998699194, 2.11394335230684, 1.8750612633917, 2.4661258704182, 2.301008280397, 2.54406804435028, 2.59105347112778, 2.30102999566398, 2.69897000433602, 2.51188336097887, 2.36170895323832, 2.06069784035361, 2.61066016308988, 2.32221929473392, 2.47712125471966, 1.94939000664491, 2.67669360962487, 2.23044892137827, 2.16905693274485, 2.19033169817029, 2.17609125905568, 2.32014628611105, 2.74818027167835, 2.25527250510331, 2.25527250510331, 2.29885307640971, 2.30102999566398, 2.41495664401567, 2.289923239524, 2.45939248775923, 1.99995656838019, 2.17606230512508, 2.25527250510331, 2.79934054945358, 2.39794000867204, 2.47712125471966, 2.6020491338302, 2.20409283840254, 2.39792263654532, 1.97772360528885, 2.25527250510331, 2.43933269383026, 2.301008280397, 2.24301323114968, 2.39794000867204, 2.82930377283102, 2.02118929906994, 2.53146614348729, 2.3835999599339, 2.69810054562339, 2.24303804868629, 2.64343293537925, 2.47712125471966, 2.24617840451086, 2.2380461031288, 2.20411998265592, 2.36170895323832, 2.32014628611105, 2.54282542695918, 2.69897000433602, 2.30102999566398, 2.37839790094814, 2.39792263654532, 2.33243845991561, 2.20411998265592, 2.59105347112778, 2.50508211450511, 2.53019969820308, 2.24303804868629, 2.57403126772772, 2.17490256217833, 2.51188336097887, 2.01994668167884, 2.17606230512508, 2.57977216766427, 2.65321251377534, 2.79587306857677, 2.54405563575924, 2.69896131835952, 2, 2.47712125471966, 2.62792884521685, 2.62838893005031, 2.5854607295085, 2.69810054562339, 2.47712125471966, 2.77451696572855, 2.64344280604489, 2.17606230512508, 2.17609125905568, 2.51719589794997, 2.25524837696186, 2.59659709562646, 2.50514997831991, 2.41497334797082, 2.3747483460101, 2.17609125905568, 1.90308998699194, 2, 2.25285303097989, 1.84503599351342, 2.4983105537896, 2.13033376849501, 2.20411998265592, 2.46982201597816, 2.34044411484012, 2.44715803134222, 2.25527250510331, 2.31175386105575, 2.31806333496276, 2.6222140229663, 2.81291335664286, 2.46239799789896, 2.17609125905568, 2.17609125905568, 2.10720996964787, 2.54400599784943, 1.95424250943932, 2.57978359661681, 2.54406804435028, 2.39794000867204, 2.29003461136252, 2.17609125905568, 2.57978359661681, 1.8431704645199, 1.47697646575953, 2.35983548233989, 2.17318626841227, 1.47639682672533, 1.85727217356404, 1.75966784468963, 1.74028371968188, 2.15986784709257, 2.77085201164214, 1.47697646575953, 2.17860289034059, 1.56761444273084, 1.90308998699194, 1.72419391951433, 1.83884909073726, 1.99978279845414, 1.56761444273084, 1.99995656838019, 1.74554320199802, 1.95904139232109, 2.39785314108861, 2.17594647009555, 2.39794000867204, 2.61277326404222, 2.34240293971527, 2.301008280397, 1.56761444273084, 2.50513640640528, 2.50513640640528, 2.62323894992999, 2.25524837696186, 2.20409283840254, 2.35983548233989, 1.92941892571429, 2.39792263654532, 1.77807886193746, 2.6283787112364, 2.38012075426844, 1.94939000664491), .Names = c("2", "6", "8", "9", "13", "15", "24", "25", "26", "30", "34", "38", "41", "42", "55", "59", "62", "67", "70", "74", "76", "79", "82", "87", "93", "100", "106", "108", "116", "117", "124", "131", "137", "142", "146", "150", "152", "156", "158", "162", "165", "168", "169", "170", "174", "177", "179", "184", "194", "197", "198", "204", "207", "216", "221", "222", "225", "231", "234", "251", "252", "253", "257", "259", "273", "283", "284", "286", "288", "293", "297", "307", "308", "323", "331", "335", "340", "351", "352", "355", "361", "368", "369", "378", "379", "389", "391", "397", "402", "404", "408", "418", "422", "430", "438", "445", "446", "452", "462", "466", "468", "469", "486", "494", "498", "508", "512", "515", "534", "541", "547", "557", "561", "566", "569", "581", "584", "585", "590", "598", "628", "629", "636", "654", "673", "675", "680", "697", "713", "727", "728", "733", "767", "775", "777", "778", "779", "781", "782", "783", "784", "785", "786", "787", "789", "790", "792", "794", "796", "797", "798", "800", "801", "806", "811", "812", "816", "820", "831", "837", "845", "857", "864", "865", "870", "874", "876", "878", "879", "889", "893", "897", "906", "908", "909", "911", "912", "914", "915", "917", "919", "920", "922", "931", "939", "951", "953", "965", "967", "968", "970", "972", "974", "976", "977", "980", "981", "983", "989", "996", "1006", "1009", "1011", "1014")), degree = 1, nprune = 33, keepxy = TRUE)
##
## Out of bag statistics:
##
## RMSE Rsquared
## 0% 0.1122 0.2593
## 2.5% 0.1141 0.3203
## 25% 0.1375 0.6717
## 50% 0.1560 0.7124
## 75% 0.1727 0.7639
## 97.5% 0.2352 0.8449
## 100% 0.2869 0.8470
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1148 Length:1148
## Class :character Class :character
## Mode :character Mode :character
##
## [1] " calling mypredict_mdl for fit:"## Warning in cbind(numTerms, numVar): number of rows of result is not a
## multiple of vector length (arg 1)
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.bagEarth
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 19.715 9.152
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9049448 0.1507259 0.6729456 0.3012982 0.2723492
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.208823 0.7245226
## label step_major step_minor label_minor bgn end
## 11 fit.models_1_RFE.X 2 9 bagEarth 215.889 271.739
## 12 fit.models_1_All.X 3 0 setup 271.740 NA
## elapsed
## 11 55.851
## 12 NA
## label step_major step_minor label_minor bgn end
## 12 fit.models_1_All.X 3 0 setup 271.740 271.747
## 13 fit.models_1_All.X 3 1 glmnet 271.747 NA
## elapsed
## 12 0.007
## 13 NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,D.ratio.wrds.stop.n.wrds.n,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,startprice.dcm2.is9,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,D.wrds.n.log,D.terms.post.stop.n.log,D.wrds.unq.n.log,D.chrs.n.log,D.chrs.uppr.n.log,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00333 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 11388 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 146 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.412207258
## D.chrs.pnct11.n.log
## -0.003522085
## D.ratio.weight.sum.wrds.n
## -0.015084699
## color.fctrSpace Gray
## 0.011874939
## color.fctrWhite
## 0.035977222
## condition.fctrFor parts or not working
## -0.364964351
## condition.fctrNew
## 0.156915781
## condition.fctrNew other (see details)
## 0.055657294
## prdl.descr.my.fctrUnknown#1
## 0.026757648
## prdl.descr.my.fctriPad1#0
## -0.359382832
## prdl.descr.my.fctriPad1#1
## -0.338420550
## prdl.descr.my.fctriPad2#0
## -0.119388133
## prdl.descr.my.fctriPad2#1
## -0.093149356
## prdl.descr.my.fctriPad3#1
## -0.060683098
## prdl.descr.my.fctriPad4#0
## 0.001029747
## prdl.descr.my.fctriPadAir#0
## 0.135656000
## prdl.descr.my.fctriPadAir#1
## 0.155660101
## prdl.descr.my.fctriPadAir2#0
## 0.228552301
## prdl.descr.my.fctriPadAir2#1
## 0.224789075
## prdl.descr.my.fctriPadmini#0
## -0.080463100
## prdl.descr.my.fctriPadmini#1
## -0.106831484
## prdl.descr.my.fctriPadmini2#0
## 0.004738705
## prdl.descr.my.fctriPadmini2#1
## 0.003289255
## prdl.descr.my.fctriPadmini3#0
## 0.084524654
## prdl.descr.my.fctriPadmini3#1
## 0.134961053
## startprice.dgt2.is9
## 0.023758934
## storage.fctr16
## -0.128539765
## storage.fctr32
## -0.052639732
## cellular.fctr0:carrier.fctrNone
## -0.006058987
## cellular.fctr1:carrier.fctrSprint
## 0.029486687
## cellular.fctr1:carrier.fctrT-Mobile
## 0.173495112
## cellular.fctr1:carrier.fctrUnknown
## 0.019212675
## cellular.fctr1:carrier.fctrVerizon
## 0.056365058
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.089856089
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.021774765
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.011577722
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.112605180
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.4118153692
## D.chrs.pnct11.n.log
## -0.0046344700
## D.ratio.weight.sum.wrds.n
## -0.0155442716
## color.fctrSpace Gray
## 0.0123497355
## color.fctrWhite
## 0.0361859933
## condition.fctrFor parts or not working
## -0.3647337399
## condition.fctrNew
## 0.1568896336
## condition.fctrNew other (see details)
## 0.0574654272
## prdl.descr.my.fctrUnknown#1
## 0.0310827125
## prdl.descr.my.fctriPad1#0
## -0.3596091724
## prdl.descr.my.fctriPad1#1
## -0.3378868769
## prdl.descr.my.fctriPad2#0
## -0.1199261327
## prdl.descr.my.fctriPad2#1
## -0.0932806218
## prdl.descr.my.fctriPad3#1
## -0.0618760405
## prdl.descr.my.fctriPad4#0
## 0.0026802156
## prdl.descr.my.fctriPadAir#0
## 0.1373060680
## prdl.descr.my.fctriPadAir#1
## 0.1587892604
## prdl.descr.my.fctriPadAir2#0
## 0.2303221576
## prdl.descr.my.fctriPadAir2#1
## 0.2266486321
## prdl.descr.my.fctriPadmini#0
## -0.0808985947
## prdl.descr.my.fctriPadmini#1
## -0.1067445831
## prdl.descr.my.fctriPadmini2#0
## 0.0056981360
## prdl.descr.my.fctriPadmini2#1
## 0.0062350032
## prdl.descr.my.fctriPadmini3#0
## 0.0868525342
## prdl.descr.my.fctriPadmini3#1
## 0.1406121977
## startprice.dcm1.is9
## -0.0009186898
## startprice.dgt2.is9
## 0.0249778097
## storage.fctr16
## -0.1292416835
## storage.fctr32
## -0.0538605629
## cellular.fctr0:carrier.fctrNone
## -0.0061307745
## cellular.fctr1:carrier.fctrSprint
## 0.0314857281
## cellular.fctr1:carrier.fctrT-Mobile
## 0.1764557942
## cellular.fctr1:carrier.fctrUnknown
## 0.0193754167
## cellular.fctr1:carrier.fctrVerizon
## 0.0582195623
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## -0.0049960590
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0957327989
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0235337891
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0155723132
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.1168123139
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 All.X.glmnet
## feats
## 1 prdl.descr.my.fctr,D.ratio.wrds.stop.n.wrds.n,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,startprice.dcm2.is9,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,D.wrds.n.log,D.terms.post.stop.n.log,D.wrds.unq.n.log,D.chrs.n.log,D.chrs.uppr.n.log,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.834 0.013
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8953388 0.1136725 0.6272593 0.3295102 0.2663481
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2393706 0.8376141 0.007433671 0.03694267
## label step_major step_minor label_minor bgn
## 13 fit.models_1_All.X 3 1 glmnet 271.747
## 14 fit.models_1_Best.Interact 4 0 setup 276.097
## end elapsed
## 13 276.096 4.35
## 14 NA NA
## label step_major step_minor label_minor bgn
## 14 fit.models_1_Best.Interact 4 0 setup 276.097
## 15 fit.models_1_RFE.X.Interact 4 1 glmnet 276.126
## end elapsed
## 14 276.126 0.029
## 15 NA NA
## [1] "fitting model: RFE.X.Interact.glmnet"
## [1] " indep_vars: .rnorm,D.chrs.n.log,D.chrs.pnct13.n.log,D.chrs.uppr.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,D.wrds.unq.n.log,startprice.dcm2.is9,storage.fctr,startprice.dcm1.is9,D.chrs.pnct11.n.log,D.ratio.weight.sum.wrds.n,prdl.descr.my.fctr*cellular.fctr,prdl.descr.my.fctr*condition.fctr,prdl.descr.my.fctr*color.fctr,prdl.descr.my.fctr*startprice.dgt2.is9,prdl.descr.my.fctr*cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00718 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 65800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 658 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.388916e+00
## D.ratio.weight.sum.wrds.n
## -1.381473e-02
## prdl.descr.my.fctriPad1#0
## -3.497532e-01
## prdl.descr.my.fctriPad1#1
## -3.209029e-01
## prdl.descr.my.fctriPad2#0
## -5.922189e-02
## prdl.descr.my.fctriPad2#1
## -6.339710e-02
## prdl.descr.my.fctriPad3#1
## -1.374492e-02
## prdl.descr.my.fctriPadAir#0
## 1.104553e-01
## prdl.descr.my.fctriPadAir#1
## 1.448767e-01
## prdl.descr.my.fctriPadAir2#0
## 2.094005e-01
## prdl.descr.my.fctriPadAir2#1
## 2.287535e-01
## prdl.descr.my.fctriPadmini#0
## -3.061786e-02
## prdl.descr.my.fctriPadmini#1
## -3.789596e-02
## prdl.descr.my.fctriPadmini3#0
## 5.120402e-02
## prdl.descr.my.fctriPadmini3#1
## 9.958473e-02
## color.fctrWhite
## 3.428095e-02
## condition.fctrFor parts or not working
## -3.496138e-01
## condition.fctrNew
## 1.602390e-01
## condition.fctrNew other (see details)
## 2.085121e-02
## startprice.dgt2.is9
## 1.421582e-02
## storage.fctr16
## -1.087145e-01
## storage.fctr32
## -1.961760e-02
## prdl.descr.my.fctrUnknown#1:cellular.fctr1
## 5.482941e-02
## prdl.descr.my.fctriPad2#0:cellular.fctr1
## -5.406020e-02
## prdl.descr.my.fctriPadAir2#0:cellular.fctr1
## 1.574477e-02
## prdl.descr.my.fctriPadmini2#0:cellular.fctr1
## 5.844105e-02
## prdl.descr.my.fctriPadmini2#1:cellular.fctr1
## 4.641456e-02
## prdl.descr.my.fctriPadmini3#0:cellular.fctr1
## 9.819228e-02
## prdl.descr.my.fctriPad4#0:cellular.fctrUnknown
## 4.337749e-02
## prdl.descr.my.fctriPadAir#0:cellular.fctrUnknown
## 1.926154e-01
## prdl.descr.my.fctriPadmini3#1:cellular.fctrUnknown
## 3.966168e-04
## cellular.fctr1:carrier.fctrT-Mobile
## 1.030112e-01
## cellular.fctr1:carrier.fctrVerizon
## 1.748327e-02
## prdl.descr.my.fctrUnknown#1:color.fctrSpace Gray
## -2.359652e-02
## prdl.descr.my.fctriPad1#0:color.fctrSpace Gray
## 1.997886e-01
## prdl.descr.my.fctriPadAir2#0:color.fctrSpace Gray
## 3.198158e-02
## prdl.descr.my.fctriPadmini#0:color.fctrSpace Gray
## -1.432653e-02
## prdl.descr.my.fctrUnknown#1:color.fctrUnknown
## 3.997622e-02
## prdl.descr.my.fctriPad2#1:color.fctrUnknown
## -2.310715e-02
## prdl.descr.my.fctriPadAir#0:color.fctrUnknown
## 2.080063e-02
## prdl.descr.my.fctriPadmini3#0:color.fctrUnknown
## 6.621730e-03
## prdl.descr.my.fctriPadmini3#1:color.fctrUnknown
## 1.477853e-04
## prdl.descr.my.fctrUnknown#1:color.fctrWhite
## 3.470588e-02
## prdl.descr.my.fctriPad2#0:color.fctrWhite
## -6.264259e-02
## prdl.descr.my.fctriPadAir2#0:color.fctrWhite
## 2.234891e-02
## prdl.descr.my.fctriPad4#1:condition.fctrFor parts or not working
## 7.720928e-02
## prdl.descr.my.fctriPadAir#0:condition.fctrFor parts or not working
## 2.234428e-04
## prdl.descr.my.fctriPadmini#0:condition.fctrFor parts or not working
## -3.178464e-01
## prdl.descr.my.fctriPadmini3#0:condition.fctrNew
## 2.520606e-03
## prdl.descr.my.fctriPad4#0:condition.fctrNew other (see details)
## 1.956710e-02
## prdl.descr.my.fctriPadAir2#0:condition.fctrNew other (see details)
## 7.155142e-02
## prdl.descr.my.fctriPad1#1:condition.fctrSeller refurbished
## -1.446310e-03
## prdl.descr.my.fctriPadmini#1:condition.fctrSeller refurbished
## 1.655489e-02
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 9.630942e-03
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 5.384348e-02
## prdl.descr.my.fctriPad1#1:cellular.fctr0:carrier.fctrNone
## -1.458300e-02
## prdl.descr.my.fctriPadAir2#1:cellular.fctr0:carrier.fctrNone
## 7.676557e-04
## prdl.descr.my.fctriPadmini#0:cellular.fctr0:carrier.fctrNone
## -1.624515e-02
## prdl.descr.my.fctriPadmini#1:cellular.fctr0:carrier.fctrNone
## -8.601653e-02
## prdl.descr.my.fctriPadmini#1:cellular.fctr1:carrier.fctrSprint
## 2.208456e-02
## prdl.descr.my.fctriPad4#0:cellular.fctr1:carrier.fctrUnknown
## 4.143237e-04
## prdl.descr.my.fctriPad4#0:cellular.fctrUnknown:carrier.fctrUnknown
## 1.291100e-03
## prdl.descr.my.fctriPadAir#0:cellular.fctrUnknown:carrier.fctrUnknown
## 3.290854e-06
## prdl.descr.my.fctriPadmini3#1:cellular.fctrUnknown:carrier.fctrUnknown
## 3.490802e-04
## prdl.descr.my.fctriPad1#0:cellular.fctr1:carrier.fctrVerizon
## 1.247018e-04
## prdl.descr.my.fctriPadAir2#0:cellular.fctr1:carrier.fctrVerizon
## 6.016349e-03
## prdl.descr.my.fctriPadmini#0:cellular.fctr1:carrier.fctrVerizon
## 2.498631e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.388573e+00
## D.ratio.weight.sum.wrds.n
## -1.385742e-02
## prdl.descr.my.fctriPad1#0
## -3.509165e-01
## prdl.descr.my.fctriPad1#1
## -3.201203e-01
## prdl.descr.my.fctriPad2#0
## -5.899545e-02
## prdl.descr.my.fctriPad2#1
## -6.400136e-02
## prdl.descr.my.fctriPad3#1
## -1.652005e-02
## prdl.descr.my.fctriPadAir#0
## 1.117116e-01
## prdl.descr.my.fctriPadAir#1
## 1.474578e-01
## prdl.descr.my.fctriPadAir2#0
## 2.083734e-01
## prdl.descr.my.fctriPadAir2#1
## 2.310508e-01
## prdl.descr.my.fctriPadmini#0
## -3.105655e-02
## prdl.descr.my.fctriPadmini#1
## -4.050064e-02
## prdl.descr.my.fctriPadmini3#0
## 5.157575e-02
## prdl.descr.my.fctriPadmini3#1
## 1.046763e-01
## color.fctrWhite
## 3.472752e-02
## condition.fctrFor parts or not working
## -3.506304e-01
## condition.fctrNew
## 1.613320e-01
## condition.fctrNew other (see details)
## 2.214479e-02
## startprice.dgt2.is9
## 1.548073e-02
## storage.fctr16
## -1.091967e-01
## storage.fctr32
## -2.058053e-02
## prdl.descr.my.fctrUnknown#1:cellular.fctr1
## 4.935693e-02
## prdl.descr.my.fctriPad2#0:cellular.fctr1
## -5.686663e-02
## prdl.descr.my.fctriPadAir2#0:cellular.fctr1
## 1.523167e-02
## prdl.descr.my.fctriPadmini2#0:cellular.fctr1
## 5.787554e-02
## prdl.descr.my.fctriPadmini2#1:cellular.fctr1
## 4.860056e-02
## prdl.descr.my.fctriPadmini3#0:cellular.fctr1
## 1.004950e-01
## prdl.descr.my.fctriPad4#0:cellular.fctrUnknown
## 4.696478e-02
## prdl.descr.my.fctriPadAir#0:cellular.fctrUnknown
## 1.981679e-01
## prdl.descr.my.fctriPadmini3#1:cellular.fctrUnknown
## 3.837490e-04
## cellular.fctr1:carrier.fctrT-Mobile
## 1.076409e-01
## cellular.fctr1:carrier.fctrVerizon
## 1.999713e-02
## prdl.descr.my.fctrUnknown#1:color.fctrSpace Gray
## -2.704866e-02
## prdl.descr.my.fctriPad1#0:color.fctrSpace Gray
## 2.035629e-01
## prdl.descr.my.fctriPadAir2#0:color.fctrSpace Gray
## 3.598708e-02
## prdl.descr.my.fctriPadmini#0:color.fctrSpace Gray
## -1.541338e-02
## prdl.descr.my.fctrUnknown#1:color.fctrUnknown
## 4.210646e-02
## prdl.descr.my.fctriPad2#1:color.fctrUnknown
## -2.350723e-02
## prdl.descr.my.fctriPadAir#0:color.fctrUnknown
## 2.054942e-02
## prdl.descr.my.fctriPadAir#1:color.fctrUnknown
## 2.437672e-04
## prdl.descr.my.fctriPadmini3#0:color.fctrUnknown
## 8.061448e-03
## prdl.descr.my.fctriPadmini3#1:color.fctrUnknown
## 1.645482e-04
## prdl.descr.my.fctrUnknown#1:color.fctrWhite
## 4.486377e-02
## prdl.descr.my.fctriPad2#0:color.fctrWhite
## -6.533509e-02
## prdl.descr.my.fctriPadAir2#0:color.fctrWhite
## 2.496598e-02
## prdl.descr.my.fctriPad4#1:condition.fctrFor parts or not working
## 8.126640e-02
## prdl.descr.my.fctriPadAir#0:condition.fctrFor parts or not working
## 2.806834e-04
## prdl.descr.my.fctriPadmini#0:condition.fctrFor parts or not working
## -3.203512e-01
## prdl.descr.my.fctriPadmini3#0:condition.fctrNew
## 2.398843e-03
## prdl.descr.my.fctriPad4#0:condition.fctrNew other (see details)
## 2.288099e-02
## prdl.descr.my.fctriPadAir2#0:condition.fctrNew other (see details)
## 7.640047e-02
## prdl.descr.my.fctriPad1#1:condition.fctrSeller refurbished
## -4.546900e-03
## prdl.descr.my.fctriPadmini#1:condition.fctrSeller refurbished
## 2.125229e-02
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 1.570383e-02
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 5.641417e-02
## prdl.descr.my.fctriPad1#1:cellular.fctr0:carrier.fctrNone
## -1.546047e-02
## prdl.descr.my.fctriPadAir2#1:cellular.fctr0:carrier.fctrNone
## 7.744621e-04
## prdl.descr.my.fctriPadmini#0:cellular.fctr0:carrier.fctrNone
## -1.684095e-02
## prdl.descr.my.fctriPadmini#1:cellular.fctr0:carrier.fctrNone
## -8.481801e-02
## prdl.descr.my.fctriPadmini#1:cellular.fctr1:carrier.fctrSprint
## 2.569478e-02
## prdl.descr.my.fctriPad4#0:cellular.fctr1:carrier.fctrUnknown
## 4.836383e-04
## prdl.descr.my.fctriPad4#0:cellular.fctrUnknown:carrier.fctrUnknown
## 1.417801e-03
## prdl.descr.my.fctriPadAir#0:cellular.fctrUnknown:carrier.fctrUnknown
## 9.447459e-06
## prdl.descr.my.fctriPadmini3#1:cellular.fctrUnknown:carrier.fctrUnknown
## 3.866341e-04
## prdl.descr.my.fctriPad1#0:cellular.fctr1:carrier.fctrVerizon
## 1.221059e-04
## prdl.descr.my.fctriPadAir2#0:cellular.fctr1:carrier.fctrVerizon
## 7.101179e-03
## prdl.descr.my.fctriPadmini#0:cellular.fctr1:carrier.fctrVerizon
## 2.712446e-02
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.Interact.glmnet
## feats
## 1 .rnorm,D.chrs.n.log,D.chrs.pnct13.n.log,D.chrs.uppr.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,D.wrds.unq.n.log,startprice.dcm2.is9,storage.fctr,startprice.dcm1.is9,D.chrs.pnct11.n.log,D.ratio.weight.sum.wrds.n,prdl.descr.my.fctr*cellular.fctr,prdl.descr.my.fctr*condition.fctr,prdl.descr.my.fctr*color.fctr,prdl.descr.my.fctr*startprice.dgt2.is9,prdl.descr.my.fctr*cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.207 0.044
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9128375 0.1182444 1.038888 0.3266861 0.2669085
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 -0.4451616 0.8268399 0.005940654 0.03802205# Check if other preProcess methods improve model performance
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse=".")
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: RFE.X.YeoJohnson.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00333 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 80 -none- numeric
## beta 11600 dgCMatrix S4
## df 80 -none- numeric
## dim 2 -none- numeric
## lambda 80 -none- numeric
## dev.ratio 80 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 145 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.4121971204
## color.fctrSpace Gray
## 0.0119722768
## color.fctrWhite
## 0.0366530913
## condition.fctrFor parts or not working
## -0.3712274517
## condition.fctrNew
## 0.1568757105
## condition.fctrNew other (see details)
## 0.0533010700
## prdl.descr.my.fctrUnknown#1
## 0.0213751534
## prdl.descr.my.fctriPad1#0
## -0.3580453288
## prdl.descr.my.fctriPad1#1
## -0.3429548487
## prdl.descr.my.fctriPad2#0
## -0.1187296491
## prdl.descr.my.fctriPad2#1
## -0.1003253507
## prdl.descr.my.fctriPad3#1
## -0.0766267875
## prdl.descr.my.fctriPad4#0
## 0.0009552261
## prdl.descr.my.fctriPadAir#0
## 0.1380158701
## prdl.descr.my.fctriPadAir#1
## 0.1437837331
## prdl.descr.my.fctriPadAir2#0
## 0.2303660718
## prdl.descr.my.fctriPadAir2#1
## 0.2233294202
## prdl.descr.my.fctriPadmini#0
## -0.0793216835
## prdl.descr.my.fctriPadmini#1
## -0.1158324651
## prdl.descr.my.fctriPadmini2#0
## 0.0060284262
## prdl.descr.my.fctriPadmini3#0
## 0.0857212998
## prdl.descr.my.fctriPadmini3#1
## 0.1311590495
## startprice.dgt2.is9
## 0.0243524525
## storage.fctr16
## -0.1264940482
## storage.fctr32
## -0.0518212691
## cellular.fctr0:carrier.fctrNone
## -0.0098076346
## cellular.fctr1:carrier.fctrSprint
## 0.0243697316
## cellular.fctr1:carrier.fctrT-Mobile
## 0.1718363028
## cellular.fctr1:carrier.fctrUnknown
## 0.0190193618
## cellular.fctr1:carrier.fctrVerizon
## 0.0574287556
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0937242445
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0162972487
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0066913269
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.1085234294
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.4114577640
## D.chrs.pnct11.n.log
## -0.0039350503
## cellular.fctrUnknown
## 0.0009253035
## color.fctrSpace Gray
## 0.0125521789
## color.fctrWhite
## 0.0369823645
## condition.fctrFor parts or not working
## -0.3715998420
## condition.fctrNew
## 0.1568994922
## condition.fctrNew other (see details)
## 0.0552014914
## prdl.descr.my.fctrUnknown#1
## 0.0244581264
## prdl.descr.my.fctriPad1#0
## -0.3584815259
## prdl.descr.my.fctriPad1#1
## -0.3429559898
## prdl.descr.my.fctriPad2#0
## -0.1195688817
## prdl.descr.my.fctriPad2#1
## -0.1013404750
## prdl.descr.my.fctriPad3#1
## -0.0789258741
## prdl.descr.my.fctriPad4#0
## 0.0021733778
## prdl.descr.my.fctriPadAir#0
## 0.1393128731
## prdl.descr.my.fctriPadAir#1
## 0.1460245599
## prdl.descr.my.fctriPadAir2#0
## 0.2316274965
## prdl.descr.my.fctriPadAir2#1
## 0.2244294687
## prdl.descr.my.fctriPadmini#0
## -0.0798982808
## prdl.descr.my.fctriPadmini#1
## -0.1167496707
## prdl.descr.my.fctriPadmini2#0
## 0.0068096110
## prdl.descr.my.fctriPadmini3#0
## 0.0877516005
## prdl.descr.my.fctriPadmini3#1
## 0.1349166729
## startprice.dgt2.is9
## 0.0252992419
## storage.fctr16
## -0.1271355703
## storage.fctr32
## -0.0529720189
## cellular.fctr0:carrier.fctrNone
## -0.0095167005
## cellular.fctr1:carrier.fctrSprint
## 0.0263680028
## cellular.fctr1:carrier.fctrT-Mobile
## 0.1750700536
## cellular.fctr1:carrier.fctrUnknown
## 0.0198040363
## cellular.fctrUnknown:carrier.fctrUnknown
## 0.0003210892
## cellular.fctr1:carrier.fctrVerizon
## 0.0598906793
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## -0.0056154205
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0995561136
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0181592261
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0105807461
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.1120023677
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.YeoJohnson.glmnet
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.474 0.271
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8944598 0.1119546 0.6306093 0.3291629 0.2664171
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2396768 0.8417826 0.007552321 0.03507816
## [1] "fitting model: RFE.X.center.scale.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in preProcess.default(method = c("center", "scale"),
## x = structure(c(-0.480112420809766, : These variables
## have zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00333 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method = c("center", :
## These variables have zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 11310 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 145 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.3003046424
## D.chrs.pnct11.n.log
## -0.0010890333
## D.ratio.weight.sum.wrds.n
## -0.0088703424
## color.fctrSpace Gray
## 0.0033764554
## color.fctrWhite
## 0.0147099665
## condition.fctrFor parts or not working
## -0.1111789416
## condition.fctrNew
## 0.0557107202
## condition.fctrNew other (see details)
## 0.0120464713
## prdl.descr.my.fctrUnknown#1
## 0.0057914284
## prdl.descr.my.fctriPad1#0
## -0.0910825209
## prdl.descr.my.fctriPad1#1
## -0.0828670702
## prdl.descr.my.fctriPad2#0
## -0.0281604309
## prdl.descr.my.fctriPad2#1
## -0.0271380986
## prdl.descr.my.fctriPad3#1
## -0.0148591170
## prdl.descr.my.fctriPad4#0
## 0.0002331537
## prdl.descr.my.fctriPadAir#0
## 0.0319975805
## prdl.descr.my.fctriPadAir#1
## 0.0216351145
## prdl.descr.my.fctriPadAir2#0
## 0.0597997031
## prdl.descr.my.fctriPadAir2#1
## 0.0312433139
## prdl.descr.my.fctriPadmini#0
## -0.0239859855
## prdl.descr.my.fctriPadmini#1
## -0.0251986572
## prdl.descr.my.fctriPadmini2#0
## 0.0010729303
## prdl.descr.my.fctriPadmini2#1
## 0.0004571718
## prdl.descr.my.fctriPadmini3#0
## 0.0164472514
## prdl.descr.my.fctriPadmini3#1
## 0.0094491637
## startprice.dgt2.is9
## 0.0085500322
## storage.fctr16
## -0.0643164245
## storage.fctr32
## -0.0186889893
## cellular.fctr0:carrier.fctrNone
## -0.0029944691
## cellular.fctr1:carrier.fctrSprint
## 0.0049942594
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0209354740
## cellular.fctr1:carrier.fctrUnknown
## 0.0035059592
## cellular.fctr1:carrier.fctrVerizon
## 0.0164213749
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0108428404
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0021507061
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0013970716
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.0156509340
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.3003046424
## D.chrs.pnct11.n.log
## -0.0014329843
## D.ratio.weight.sum.wrds.n
## -0.0091405874
## color.fctrSpace Gray
## 0.0035114564
## color.fctrWhite
## 0.0147953268
## condition.fctrFor parts or not working
## -0.1111086906
## condition.fctrNew
## 0.0557014368
## condition.fctrNew other (see details)
## 0.0124378238
## prdl.descr.my.fctrUnknown#1
## 0.0067275460
## prdl.descr.my.fctriPad1#0
## -0.0911398850
## prdl.descr.my.fctriPad1#1
## -0.0827363927
## prdl.descr.my.fctriPad2#0
## -0.0282873304
## prdl.descr.my.fctriPad2#1
## -0.0271763417
## prdl.descr.my.fctriPad3#1
## -0.0151512258
## prdl.descr.my.fctriPad4#0
## 0.0006068504
## prdl.descr.my.fctriPadAir#0
## 0.0323867870
## prdl.descr.my.fctriPadAir#1
## 0.0220700347
## prdl.descr.my.fctriPadAir2#0
## 0.0602627782
## prdl.descr.my.fctriPadAir2#1
## 0.0315017727
## prdl.descr.my.fctriPadmini#0
## -0.0241158061
## prdl.descr.my.fctriPadmini#1
## -0.0251781595
## prdl.descr.my.fctriPadmini2#0
## 0.0012901634
## prdl.descr.my.fctriPadmini2#1
## 0.0008665998
## prdl.descr.my.fctriPadmini3#0
## 0.0169002225
## prdl.descr.my.fctriPadmini3#1
## 0.0098448230
## startprice.dcm1.is9
## -0.0004357097
## startprice.dgt2.is9
## 0.0089886640
## storage.fctr16
## -0.0646676379
## storage.fctr32
## -0.0191224281
## cellular.fctr0:carrier.fctrNone
## -0.0030299481
## cellular.fctr1:carrier.fctrSprint
## 0.0053328437
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0212927364
## cellular.fctr1:carrier.fctrUnknown
## 0.0035356566
## cellular.fctr1:carrier.fctrVerizon
## 0.0169616656
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr2
## -0.0006943996
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.0115519768
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0023244459
## prdl.descr.my.fctriPad2#1:.clusterid.fctr3
## 0.0018790948
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.0162356813
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.center.scale.glmnet
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.208 0.043
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8953388 0.1133377 0.6336859 0.3295102 0.2663481
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2400704 0.8385459 0.007592452 0.03745714
## [1] "fitting model: RFE.X.range.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5## [1] "fitting model: RFE.X.pca.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5## [1] "fitting model: RFE.X.ica.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5## [1] "fitting model: RFE.X.spatialSign.glmnet"
## [1] " indep_vars: prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"## Warning in preProcess.default(method = "spatialSign",
## x = structure(c(-0.480112420809766, : These variables
## have zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00328 on full training set## Warning in preProcess.default(thresh = 0.95, k =
## 5, method = "spatialSign", : These variables have
## zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr2,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr2,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr3,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr3,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr4,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr4,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr4,
## prdl.descr.my.fctrUnknown#0:.clusterid.fctr5,
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad1#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad3#1:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#0:.clusterid.fctr5,
## prdl.descr.my.fctriPad4#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#0:.clusterid.fctr5,
## prdl.descr.my.fctriPadmini3#1:.clusterid.fctr5
## Length Class Mode
## a0 83 -none- numeric
## beta 12035 dgCMatrix S4
## df 83 -none- numeric
## dim 2 -none- numeric
## lambda 83 -none- numeric
## dev.ratio 83 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 145 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.313019142
## D.chrs.pnct11.n.log
## -0.069563408
## D.chrs.pnct13.n.log
## -0.010638975
## D.ratio.weight.sum.wrds.n
## -0.256435872
## D.weight.sum.stem.stop.Ratio
## 0.005334369
## cellular.fctrUnknown
## 0.028048116
## color.fctrGold
## 0.092016728
## color.fctrSpace Gray
## 0.012558396
## color.fctrWhite
## 0.118111523
## condition.fctrFor parts or not working
## -0.814019492
## condition.fctrManufacturer refurbished
## 0.028499130
## condition.fctrNew
## 0.347367780
## condition.fctrNew other (see details)
## 0.116176982
## prdl.descr.my.fctrUnknown#1
## 0.156958355
## prdl.descr.my.fctriPad1#0
## -0.528548164
## prdl.descr.my.fctriPad1#1
## -0.537074312
## prdl.descr.my.fctriPad2#0
## -0.137165531
## prdl.descr.my.fctriPad2#1
## -0.125380985
## prdl.descr.my.fctriPad3#1
## -0.049252415
## prdl.descr.my.fctriPad4#0
## 0.007083773
## prdl.descr.my.fctriPadAir#0
## 0.231278798
## prdl.descr.my.fctriPadAir#1
## 0.272299863
## prdl.descr.my.fctriPadAir2#0
## 0.384434708
## prdl.descr.my.fctriPadAir2#1
## 0.372794097
## prdl.descr.my.fctriPadmini#0
## -0.125120420
## prdl.descr.my.fctriPadmini#1
## -0.147375947
## prdl.descr.my.fctriPadmini2#0
## 0.011305955
## prdl.descr.my.fctriPadmini2#1
## 0.115381817
## prdl.descr.my.fctriPadmini3#0
## 0.149660027
## prdl.descr.my.fctriPadmini3#1
## 0.236361216
## startprice.dgt2.is9
## 0.068258437
## storage.fctr16
## -0.411084120
## storage.fctr32
## -0.129320374
## cellular.fctr0:carrier.fctrNone
## -0.008057713
## cellular.fctr1:carrier.fctrSprint
## 0.085907095
## cellular.fctr1:carrier.fctrT-Mobile
## 0.283262340
## cellular.fctr1:carrier.fctrUnknown
## 0.098646360
## cellular.fctrUnknown:carrier.fctrUnknown
## 0.005732715
## cellular.fctr1:carrier.fctrVerizon
## 0.081405897
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.144535093
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.143224733
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2
## -0.021952941
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.154667148
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr3
## -0.010436462
## prdl.descr.my.fctriPad1#1:.clusterid.fctr4
## -0.133659027
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.313116236
## D.chrs.pnct11.n.log
## -0.071126845
## D.chrs.pnct13.n.log
## -0.016257203
## D.ratio.weight.sum.wrds.n
## -0.259522982
## D.weight.sum.stem.stop.Ratio
## 0.005876661
## cellular.fctrUnknown
## 0.029202846
## color.fctrGold
## 0.092634273
## color.fctrSpace Gray
## 0.013053819
## color.fctrWhite
## 0.118489604
## condition.fctrFor parts or not working
## -0.816230948
## condition.fctrManufacturer refurbished
## 0.029489731
## condition.fctrNew
## 0.346901635
## condition.fctrNew other (see details)
## 0.118735680
## prdl.descr.my.fctrUnknown#1
## 0.159307775
## prdl.descr.my.fctriPad1#0
## -0.530177981
## prdl.descr.my.fctriPad1#1
## -0.535523901
## prdl.descr.my.fctriPad2#0
## -0.139017146
## prdl.descr.my.fctriPad2#1
## -0.123719490
## prdl.descr.my.fctriPad3#1
## -0.050754346
## prdl.descr.my.fctriPad4#0
## 0.007472389
## prdl.descr.my.fctriPadAir#0
## 0.231995471
## prdl.descr.my.fctriPadAir#1
## 0.275246416
## prdl.descr.my.fctriPadAir2#0
## 0.385081584
## prdl.descr.my.fctriPadAir2#1
## 0.376891447
## prdl.descr.my.fctriPadmini#0
## -0.127125524
## prdl.descr.my.fctriPadmini#1
## -0.147682531
## prdl.descr.my.fctriPadmini2#0
## 0.011836386
## prdl.descr.my.fctriPadmini2#1
## 0.123409726
## prdl.descr.my.fctriPadmini3#0
## 0.151105096
## prdl.descr.my.fctriPadmini3#1
## 0.240840421
## startprice.dgt2.is9
## 0.070080600
## storage.fctr16
## -0.412303606
## storage.fctr32
## -0.131993441
## cellular.fctr0:carrier.fctrNone
## -0.008015479
## cellular.fctr1:carrier.fctrSprint
## 0.087945934
## cellular.fctr1:carrier.fctrT-Mobile
## 0.285430946
## cellular.fctr1:carrier.fctrUnknown
## 0.099430307
## cellular.fctrUnknown:carrier.fctrUnknown
## 0.006161364
## cellular.fctr1:carrier.fctrVerizon
## 0.084702970
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.149042876
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.143996432
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2
## -0.030562596
## prdl.descr.my.fctriPad4#1:.clusterid.fctr3
## 0.159945361
## prdl.descr.my.fctriPadmini#1:.clusterid.fctr3
## -0.011851488
## prdl.descr.my.fctriPad1#1:.clusterid.fctr4
## -0.136419037
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.spatialSign.glmnet
## feats
## 1 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.406 0.069
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8980212 0.1173961 0.6430741 0.3240306 0.2674343
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2338599 0.822907 0.008371016 0.04305471 # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.chrs.uppr.n.log, glb_allobs_df$A.chrs.uppr.n.log)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glb_mdl_methods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO.lm MFO.lm
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.svmLinear RFE.X.svmLinear
## RFE.X.svmLinear2 RFE.X.svmLinear2
## RFE.X.earth RFE.X.earth
## RFE.X.bagEarth RFE.X.bagEarth
## All.X.glmnet All.X.glmnet
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet
## RFE.X.YeoJohnson.glmnet RFE.X.YeoJohnson.glmnet
## RFE.X.center.scale.glmnet RFE.X.center.scale.glmnet
## RFE.X.spatialSign.glmnet RFE.X.spatialSign.glmnet
## feats
## MFO.lm .rnorm
## Max.cor.Y.rcv.1X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.1X1.cp.0.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## Interact.High.cor.Y.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum,prdl.descr.my.fctr:D.weight.post.stop.sum,prdl.descr.my.fctr:startprice.dcm1.is9,prdl.descr.my.fctr:D.chrs.uppr.n.log,prdl.descr.my.fctr:D.wrds.unq.n.log
## Low.cor.X.glmnet prdl.descr.my.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glm prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.rpart prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.gbm prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.rf prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.svmLinear prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.svmLinear2 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.earth prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.bagEarth prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## All.X.glmnet prdl.descr.my.fctr,D.ratio.wrds.stop.n.wrds.n,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,startprice.dcm2.is9,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,D.wrds.n.log,D.terms.post.stop.n.log,D.wrds.unq.n.log,D.chrs.n.log,D.chrs.uppr.n.log,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.Interact.glmnet .rnorm,D.chrs.n.log,D.chrs.pnct13.n.log,D.chrs.uppr.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,D.wrds.unq.n.log,startprice.dcm2.is9,storage.fctr,startprice.dcm1.is9,D.chrs.pnct11.n.log,D.ratio.weight.sum.wrds.n,prdl.descr.my.fctr*cellular.fctr,prdl.descr.my.fctr*condition.fctr,prdl.descr.my.fctr*color.fctr,prdl.descr.my.fctr*startprice.dgt2.is9,prdl.descr.my.fctr*cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.YeoJohnson.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.center.scale.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.spatialSign.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.582
## Max.cor.Y.rcv.1X1.glmnet 0 0.765
## Max.cor.Y.rcv.3X1.glmnet 25 1.210
## Max.cor.Y.rcv.3X3.glmnet 25 2.353
## Max.cor.Y.rcv.3X5.glmnet 25 2.576
## Max.cor.Y.rcv.5X1.glmnet 25 1.368
## Max.cor.Y.rcv.5X3.glmnet 25 1.980
## Max.cor.Y.rcv.5X5.glmnet 25 2.349
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 1.377
## Max.cor.Y.rpart 5 1.326
## Interact.High.cor.Y.glmnet 25 2.224
## Low.cor.X.glmnet 25 1.767
## RFE.X.glm 1 1.379
## RFE.X.glmnet 25 1.793
## RFE.X.rpart 5 1.792
## RFE.X.gbm 125 17.745
## RFE.X.rf 5 5.936
## RFE.X.svmLinear 5 2.598
## RFE.X.svmLinear2 5 1.878
## RFE.X.earth 5 3.675
## RFE.X.bagEarth 1 19.715
## All.X.glmnet 25 1.834
## RFE.X.Interact.glmnet 25 2.207
## RFE.X.YeoJohnson.glmnet 25 4.474
## RFE.X.center.scale.glmnet 25 2.208
## RFE.X.spatialSign.glmnet 25 2.406
## min.elapsedtime.final max.R.sq.fit
## MFO.lm 0.003 0.003005849
## Max.cor.Y.rcv.1X1.glmnet 0.012 0.698289322
## Max.cor.Y.rcv.3X1.glmnet 0.007 0.691838897
## Max.cor.Y.rcv.3X3.glmnet 0.008 0.691838897
## Max.cor.Y.rcv.3X5.glmnet 0.008 0.691838897
## Max.cor.Y.rcv.5X1.glmnet 0.007 0.691838897
## Max.cor.Y.rcv.5X3.glmnet 0.006 0.691838897
## Max.cor.Y.rcv.5X5.glmnet 0.007 0.691838897
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.027 0.674249249
## Max.cor.Y.rpart 0.016 0.513541388
## Interact.High.cor.Y.glmnet 0.046 0.708296228
## Low.cor.X.glmnet 0.011 0.895338827
## RFE.X.glm 0.055 0.910073107
## RFE.X.glmnet 0.012 0.895338827
## RFE.X.rpart 0.053 0.661480554
## RFE.X.gbm 0.312 0.864336819
## RFE.X.rf 1.778 0.946868942
## RFE.X.svmLinear 0.045 0.876588532
## RFE.X.svmLinear2 0.044 0.883480888
## RFE.X.earth 0.240 0.810065644
## RFE.X.bagEarth 9.152 0.904944786
## All.X.glmnet 0.013 0.895338827
## RFE.X.Interact.glmnet 0.044 0.912837527
## RFE.X.YeoJohnson.glmnet 0.271 0.894459808
## RFE.X.center.scale.glmnet 0.043 0.895338827
## RFE.X.spatialSign.glmnet 0.069 0.898021182
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## MFO.lm 0.2800985 -0.001929766 -0.002916031
## Max.cor.Y.rcv.1X1.glmnet 0.1540848 0.665315477 0.305079502
## Max.cor.Y.rcv.3X1.glmnet 0.1759578 0.658160088 0.297034428
## Max.cor.Y.rcv.3X3.glmnet 0.1741121 0.658160088 0.297034428
## Max.cor.Y.rcv.3X5.glmnet 0.1743007 0.658160088 0.297034428
## Max.cor.Y.rcv.5X1.glmnet 0.1677621 0.658160088 0.297034428
## Max.cor.Y.rcv.5X3.glmnet 0.1686714 0.658160088 0.297034428
## Max.cor.Y.rcv.5X5.glmnet 0.1694123 0.658160088 0.297034428
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.1601058 NA 0.262582396
## Max.cor.Y.rpart 0.2184953 NA 0.185842742
## Interact.High.cor.Y.glmnet 0.1793821 0.430616677 0.280690344
## Low.cor.X.glmnet 0.1135832 0.673135106 0.329510154
## RFE.X.glm 0.1542425 0.868667919 0.292053329
## RFE.X.glmnet 0.1133377 0.633685895 0.329510154
## RFE.X.rpart 0.1770237 NA 0.157116372
## RFE.X.gbm 0.2006895 0.533226681 0.197771740
## RFE.X.rf 0.1339398 0.817193139 0.232062026
## RFE.X.svmLinear 0.1297514 0.861588243 0.319962152
## RFE.X.svmLinear2 0.1278480 0.869318344 0.325170827
## RFE.X.earth 0.1334478 0.346497046 0.266269664
## RFE.X.bagEarth 0.1507259 0.672945619 0.301298226
## All.X.glmnet 0.1136725 0.627259332 0.329510154
## RFE.X.Interact.glmnet 0.1182444 1.038887873 0.326686069
## RFE.X.YeoJohnson.glmnet 0.1119546 0.630609328 0.329162866
## RFE.X.center.scale.glmnet 0.1133377 0.633685895 0.329510154
## RFE.X.spatialSign.glmnet 0.1173961 0.643074137 0.324030623
## min.RMSE.OOB max.Adj.R.sq.OOB
## MFO.lm 0.3257510 -0.003730747
## Max.cor.Y.rcv.1X1.glmnet 0.2711572 0.293612167
## Max.cor.Y.rcv.3X1.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.3X3.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.3X5.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.5X1.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.5X3.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.5X5.glmnet 0.2727222 0.285434336
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.2793253 NA
## Max.cor.Y.rpart 0.2934997 NA
## Interact.High.cor.Y.glmnet 0.2758745 0.217838044
## Low.cor.X.glmnet 0.2663481 0.244932824
## RFE.X.glm 0.2736868 0.253261731
## RFE.X.glmnet 0.2663481 0.240070386
## RFE.X.rpart 0.2986327 NA
## RFE.X.gbm 0.2914982 0.091594471
## RFE.X.rf 0.2858241 0.130423176
## RFE.X.svmLinear 0.2687337 0.307597827
## RFE.X.svmLinear2 0.2676586 0.312901206
## RFE.X.earth 0.2786261 0.169158296
## RFE.X.bagEarth 0.2723492 0.208822991
## All.X.glmnet 0.2663481 0.239370635
## RFE.X.Interact.glmnet 0.2669085 -0.445161607
## RFE.X.YeoJohnson.glmnet 0.2664171 0.239676771
## RFE.X.center.scale.glmnet 0.2663481 0.240070386
## RFE.X.spatialSign.glmnet 0.2674343 0.233859915
## max.Rsquared.fit min.RMSESD.fit
## MFO.lm NA NA
## Max.cor.Y.rcv.1X1.glmnet NA NA
## Max.cor.Y.rcv.3X1.glmnet 0.6288608 0.011078714
## Max.cor.Y.rcv.3X3.glmnet 0.6274240 0.011432922
## Max.cor.Y.rcv.3X5.glmnet 0.6240784 0.012242006
## Max.cor.Y.rcv.5X1.glmnet 0.6515568 0.036632478
## Max.cor.Y.rcv.5X3.glmnet 0.6469703 0.028120900
## Max.cor.Y.rcv.5X5.glmnet 0.6420291 0.024460186
## Max.cor.Y.rcv.1X1.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.4057368 0.024438493
## Interact.High.cor.Y.glmnet 0.6042900 0.010509388
## Low.cor.X.glmnet 0.8378099 0.007517978
## RFE.X.glm 0.7514847 0.038767873
## RFE.X.glmnet 0.8385459 0.007592452
## RFE.X.rpart 0.6109292 0.005823641
## RFE.X.gbm 0.4901601 0.023952401
## RFE.X.rf 0.7720234 NA
## RFE.X.svmLinear 0.7954752 0.010576176
## RFE.X.svmLinear2 0.7947689 0.009445519
## RFE.X.earth 0.7737232 0.014021022
## RFE.X.bagEarth 0.7245226 NA
## All.X.glmnet 0.8376141 0.007433671
## RFE.X.Interact.glmnet 0.8268399 0.005940654
## RFE.X.YeoJohnson.glmnet 0.8417826 0.007552321
## RFE.X.center.scale.glmnet 0.8385459 0.007592452
## RFE.X.spatialSign.glmnet 0.8229070 0.008371016
## max.RsquaredSD.fit min.aic.fit
## MFO.lm NA NA
## Max.cor.Y.rcv.1X1.glmnet NA NA
## Max.cor.Y.rcv.3X1.glmnet 0.03020720 NA
## Max.cor.Y.rcv.3X3.glmnet 0.06492936 NA
## Max.cor.Y.rcv.3X5.glmnet 0.06926107 NA
## Max.cor.Y.rcv.5X1.glmnet 0.12303698 NA
## Max.cor.Y.rcv.5X3.glmnet 0.09441314 NA
## Max.cor.Y.rcv.5X5.glmnet 0.09766685 NA
## Max.cor.Y.rcv.1X1.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.12332025 NA
## Interact.High.cor.Y.glmnet 0.08091175 NA
## Low.cor.X.glmnet 0.03713866 NA
## RFE.X.glm 0.08601771 -299.0721
## RFE.X.glmnet 0.03745714 NA
## RFE.X.rpart 0.08368777 NA
## RFE.X.gbm 0.10140185 NA
## RFE.X.rf NA NA
## RFE.X.svmLinear 0.03892115 NA
## RFE.X.svmLinear2 0.03760688 NA
## RFE.X.earth 0.06316611 NA
## RFE.X.bagEarth NA NA
## All.X.glmnet 0.03694267 NA
## RFE.X.Interact.glmnet 0.03802205 NA
## RFE.X.YeoJohnson.glmnet 0.03507816 NA
## RFE.X.center.scale.glmnet 0.03745714 NA
## RFE.X.spatialSign.glmnet 0.04305471 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE, label.minor="teardown")## label step_major step_minor label_minor bgn
## 15 fit.models_1_RFE.X.Interact 4 1 glmnet 276.126
## 16 fit.models_1_end 5 0 teardown 299.576
## end elapsed
## 15 299.575 23.449
## 16 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 11 fit.models 7 1 1 158.014 299.584 141.57
## 12 fit.models 7 2 2 299.584 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "id", FALSE]
stats_mdl_df <- data.frame()
for (mdl_id in stats_df$id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[mdl_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, mdl_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (mdl_id in stats_df$id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[mdl_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, mdl_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~mdl_id, glb_models_df[, c("id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-mdl_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO.lm MFO.lm
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.svmLinear RFE.X.svmLinear
## RFE.X.svmLinear2 RFE.X.svmLinear2
## RFE.X.earth RFE.X.earth
## RFE.X.bagEarth RFE.X.bagEarth
## All.X.glmnet All.X.glmnet
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet
## RFE.X.YeoJohnson.glmnet RFE.X.YeoJohnson.glmnet
## RFE.X.center.scale.glmnet RFE.X.center.scale.glmnet
## RFE.X.spatialSign.glmnet RFE.X.spatialSign.glmnet
## feats
## MFO.lm .rnorm
## Max.cor.Y.rcv.1X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.3X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X1.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X3.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.5X5.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rcv.1X1.cp.0.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## Max.cor.Y.rpart prdl.descr.my.fctr,D.weight.post.stop.sum
## Interact.High.cor.Y.glmnet prdl.descr.my.fctr,D.weight.post.stop.sum,prdl.descr.my.fctr:D.weight.post.stop.sum,prdl.descr.my.fctr:startprice.dcm1.is9,prdl.descr.my.fctr:D.chrs.uppr.n.log,prdl.descr.my.fctr:D.wrds.unq.n.log
## Low.cor.X.glmnet prdl.descr.my.fctr,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glm prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.rpart prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.gbm prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.rf prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.svmLinear prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.svmLinear2 prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.earth prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.bagEarth prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## All.X.glmnet prdl.descr.my.fctr,D.ratio.wrds.stop.n.wrds.n,color.fctr,D.weight.sum.stem.stop.Ratio,condition.fctr,startprice.dgt1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,.rnorm,startprice.dcm2.is9,storage.fctr,D.chrs.pnct11.n.log,startprice.dcm1.is9,D.wrds.n.log,D.terms.post.stop.n.log,D.wrds.unq.n.log,D.chrs.n.log,D.chrs.uppr.n.log,cellular.fctr,D.ratio.weight.sum.wrds.n,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.Interact.glmnet .rnorm,D.chrs.n.log,D.chrs.pnct13.n.log,D.chrs.uppr.n.log,D.ratio.wrds.stop.n.wrds.n,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.post.stop.sum,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,D.wrds.unq.n.log,startprice.dcm2.is9,storage.fctr,startprice.dcm1.is9,D.chrs.pnct11.n.log,D.ratio.weight.sum.wrds.n,prdl.descr.my.fctr*cellular.fctr,prdl.descr.my.fctr*condition.fctr,prdl.descr.my.fctr*color.fctr,prdl.descr.my.fctr*startprice.dgt2.is9,prdl.descr.my.fctr*cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.YeoJohnson.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.center.scale.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.spatialSign.glmnet prdl.descr.my.fctr,condition.fctr,storage.fctr,D.weight.post.stem.sum,D.weight.post.stop.sum,cellular.fctr,color.fctr,D.chrs.uppr.n.log,D.terms.post.stop.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,D.wrds.unq.n.log,D.chrs.n.log,D.weight.sum.stem.stop.Ratio,D.wrds.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.chrs.pnct13.n.log,D.chrs.pnct11.n.log,startprice.dcm2.is9,.rnorm,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns max.R.sq.fit max.Adj.R.sq.fit
## MFO.lm 0 0.003005849 -0.001929766
## Max.cor.Y.rcv.1X1.glmnet 0 0.698289322 0.665315477
## Max.cor.Y.rcv.3X1.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.3X3.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.3X5.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.5X1.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.5X3.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.5X5.glmnet 25 0.691838897 0.658160088
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.674249249 NA
## Max.cor.Y.rpart 5 0.513541388 NA
## Interact.High.cor.Y.glmnet 25 0.708296228 0.430616677
## Low.cor.X.glmnet 25 0.895338827 0.673135106
## RFE.X.glm 1 0.910073107 0.868667919
## RFE.X.glmnet 25 0.895338827 0.633685895
## RFE.X.rpart 5 0.661480554 NA
## RFE.X.gbm 125 0.864336819 0.533226681
## RFE.X.rf 5 0.946868942 0.817193139
## RFE.X.svmLinear 5 0.876588532 0.861588243
## RFE.X.svmLinear2 5 0.883480888 0.869318344
## RFE.X.earth 5 0.810065644 0.346497046
## RFE.X.bagEarth 1 0.904944786 0.672945619
## All.X.glmnet 25 0.895338827 0.627259332
## RFE.X.Interact.glmnet 25 0.912837527 1.038887873
## RFE.X.YeoJohnson.glmnet 25 0.894459808 0.630609328
## RFE.X.center.scale.glmnet 25 0.895338827 0.633685895
## RFE.X.spatialSign.glmnet 25 0.898021182 0.643074137
## max.R.sq.OOB max.Adj.R.sq.OOB
## MFO.lm -0.002916031 -0.003730747
## Max.cor.Y.rcv.1X1.glmnet 0.305079502 0.293612167
## Max.cor.Y.rcv.3X1.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.3X3.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.3X5.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.5X1.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.5X3.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.5X5.glmnet 0.297034428 0.285434336
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.262582396 NA
## Max.cor.Y.rpart 0.185842742 NA
## Interact.High.cor.Y.glmnet 0.280690344 0.217838044
## Low.cor.X.glmnet 0.329510154 0.244932824
## RFE.X.glm 0.292053329 0.253261731
## RFE.X.glmnet 0.329510154 0.240070386
## RFE.X.rpart 0.157116372 NA
## RFE.X.gbm 0.197771740 0.091594471
## RFE.X.rf 0.232062026 0.130423176
## RFE.X.svmLinear 0.319962152 0.307597827
## RFE.X.svmLinear2 0.325170827 0.312901206
## RFE.X.earth 0.266269664 0.169158296
## RFE.X.bagEarth 0.301298226 0.208822991
## All.X.glmnet 0.329510154 0.239370635
## RFE.X.Interact.glmnet 0.326686069 -0.445161607
## RFE.X.YeoJohnson.glmnet 0.329162866 0.239676771
## RFE.X.center.scale.glmnet 0.329510154 0.240070386
## RFE.X.spatialSign.glmnet 0.324030623 0.233859915
## max.Rsquared.fit inv.elapsedtime.everything
## MFO.lm NA 1.7182131
## Max.cor.Y.rcv.1X1.glmnet NA 1.3071895
## Max.cor.Y.rcv.3X1.glmnet 0.6288608 0.8264463
## Max.cor.Y.rcv.3X3.glmnet 0.6274240 0.4249894
## Max.cor.Y.rcv.3X5.glmnet 0.6240784 0.3881988
## Max.cor.Y.rcv.5X1.glmnet 0.6515568 0.7309942
## Max.cor.Y.rcv.5X3.glmnet 0.6469703 0.5050505
## Max.cor.Y.rcv.5X5.glmnet 0.6420291 0.4257131
## Max.cor.Y.rcv.1X1.cp.0.rpart NA 0.7262164
## Max.cor.Y.rpart 0.4057368 0.7541478
## Interact.High.cor.Y.glmnet 0.6042900 0.4496403
## Low.cor.X.glmnet 0.8378099 0.5659310
## RFE.X.glm 0.7514847 0.7251632
## RFE.X.glmnet 0.8385459 0.5577245
## RFE.X.rpart 0.6109292 0.5580357
## RFE.X.gbm 0.4901601 0.0563539
## RFE.X.rf 0.7720234 0.1684636
## RFE.X.svmLinear 0.7954752 0.3849115
## RFE.X.svmLinear2 0.7947689 0.5324814
## RFE.X.earth 0.7737232 0.2721088
## RFE.X.bagEarth 0.7245226 0.0507228
## All.X.glmnet 0.8376141 0.5452563
## RFE.X.Interact.glmnet 0.8268399 0.4531038
## RFE.X.YeoJohnson.glmnet 0.8417826 0.2235136
## RFE.X.center.scale.glmnet 0.8385459 0.4528986
## RFE.X.spatialSign.glmnet 0.8229070 0.4156276
## inv.elapsedtime.final inv.RMSE.fit
## MFO.lm 333.3333333 3.570173
## Max.cor.Y.rcv.1X1.glmnet 83.3333333 6.489933
## Max.cor.Y.rcv.3X1.glmnet 142.8571429 5.683180
## Max.cor.Y.rcv.3X3.glmnet 125.0000000 5.743426
## Max.cor.Y.rcv.3X5.glmnet 125.0000000 5.737212
## Max.cor.Y.rcv.5X1.glmnet 142.8571429 5.960823
## Max.cor.Y.rcv.5X3.glmnet 166.6666667 5.928689
## Max.cor.Y.rcv.5X5.glmnet 142.8571429 5.902759
## Max.cor.Y.rcv.1X1.cp.0.rpart 37.0370370 6.245868
## Max.cor.Y.rpart 62.5000000 4.576758
## Interact.High.cor.Y.glmnet 21.7391304 5.574692
## Low.cor.X.glmnet 90.9090909 8.804116
## RFE.X.glm 18.1818182 6.483297
## RFE.X.glmnet 83.3333333 8.823190
## RFE.X.rpart 18.8679245 5.648960
## RFE.X.gbm 3.2051282 4.982821
## RFE.X.rf 0.5624297 7.466042
## RFE.X.svmLinear 22.2222222 7.707044
## RFE.X.svmLinear2 22.7272727 7.821789
## RFE.X.earth 4.1666667 7.493567
## RFE.X.bagEarth 0.1092657 6.634561
## All.X.glmnet 76.9230769 8.797200
## RFE.X.Interact.glmnet 22.7272727 8.457059
## RFE.X.YeoJohnson.glmnet 3.6900369 8.932190
## RFE.X.center.scale.glmnet 23.2558140 8.823190
## RFE.X.spatialSign.glmnet 14.4927536 8.518174
## inv.RMSE.OOB inv.aic.fit
## MFO.lm 3.069829 NA
## Max.cor.Y.rcv.1X1.glmnet 3.687898 NA
## Max.cor.Y.rcv.3X1.glmnet 3.666734 NA
## Max.cor.Y.rcv.3X3.glmnet 3.666734 NA
## Max.cor.Y.rcv.3X5.glmnet 3.666734 NA
## Max.cor.Y.rcv.5X1.glmnet 3.666734 NA
## Max.cor.Y.rcv.5X3.glmnet 3.666734 NA
## Max.cor.Y.rcv.5X5.glmnet 3.666734 NA
## Max.cor.Y.rcv.1X1.cp.0.rpart 3.580055 NA
## Max.cor.Y.rpart 3.407158 NA
## Interact.High.cor.Y.glmnet 3.624837 NA
## Low.cor.X.glmnet 3.754485 NA
## RFE.X.glm 3.653812 -0.003343675
## RFE.X.glmnet 3.754485 NA
## RFE.X.rpart 3.348595 NA
## RFE.X.gbm 3.430553 NA
## RFE.X.rf 3.498656 NA
## RFE.X.svmLinear 3.721155 NA
## RFE.X.svmLinear2 3.736102 NA
## RFE.X.earth 3.589039 NA
## RFE.X.bagEarth 3.671756 NA
## All.X.glmnet 3.754485 NA
## RFE.X.Interact.glmnet 3.746603 NA
## RFE.X.YeoJohnson.glmnet 3.753513 NA
## RFE.X.center.scale.glmnet 3.754485 NA
## RFE.X.spatialSign.glmnet 3.739237 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 26. Consider specifying shapes manually if you must have them.## Warning: Removed 34 rows containing missing values (geom_path).## Warning: Removed 232 rows containing missing values (geom_point).## Warning: Removed 34 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 26. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 3 rows containing missing values (position_stack).## Warning: Removed 12 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 3 rows containing missing values (position_stack).## Warning: Removed 12 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)[, dsp_models_cols])## id min.RMSE.fit max.Adj.R.sq.fit max.R.sq.fit
## 24 RFE.X.YeoJohnson.glmnet 0.1119546 0.630609328 0.894459808
## 14 RFE.X.glmnet 0.1133377 0.633685895 0.895338827
## 25 RFE.X.center.scale.glmnet 0.1133377 0.633685895 0.895338827
## 12 Low.cor.X.glmnet 0.1135832 0.673135106 0.895338827
## 22 All.X.glmnet 0.1136725 0.627259332 0.895338827
## 26 RFE.X.spatialSign.glmnet 0.1173961 0.643074137 0.898021182
## 23 RFE.X.Interact.glmnet 0.1182444 1.038887873 0.912837527
## 19 RFE.X.svmLinear2 0.1278480 0.869318344 0.883480888
## 18 RFE.X.svmLinear 0.1297514 0.861588243 0.876588532
## 20 RFE.X.earth 0.1334478 0.346497046 0.810065644
## 17 RFE.X.rf 0.1339398 0.817193139 0.946868942
## 21 RFE.X.bagEarth 0.1507259 0.672945619 0.904944786
## 2 Max.cor.Y.rcv.1X1.glmnet 0.1540848 0.665315477 0.698289322
## 13 RFE.X.glm 0.1542425 0.868667919 0.910073107
## 9 Max.cor.Y.rcv.1X1.cp.0.rpart 0.1601058 NA 0.674249249
## 6 Max.cor.Y.rcv.5X1.glmnet 0.1677621 0.658160088 0.691838897
## 7 Max.cor.Y.rcv.5X3.glmnet 0.1686714 0.658160088 0.691838897
## 8 Max.cor.Y.rcv.5X5.glmnet 0.1694123 0.658160088 0.691838897
## 4 Max.cor.Y.rcv.3X3.glmnet 0.1741121 0.658160088 0.691838897
## 5 Max.cor.Y.rcv.3X5.glmnet 0.1743007 0.658160088 0.691838897
## 3 Max.cor.Y.rcv.3X1.glmnet 0.1759578 0.658160088 0.691838897
## 15 RFE.X.rpart 0.1770237 NA 0.661480554
## 11 Interact.High.cor.Y.glmnet 0.1793821 0.430616677 0.708296228
## 16 RFE.X.gbm 0.2006895 0.533226681 0.864336819
## 10 Max.cor.Y.rpart 0.2184953 NA 0.513541388
## 1 MFO.lm 0.2800985 -0.001929766 0.003005849print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 26. Consider specifying shapes manually if you must have them.## Warning: Removed 3 rows containing missing values (geom_path).## Warning: Removed 81 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 26. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.fit - max.Adj.R.sq.fit - max.R.sq.fit
## <environment: 0x7f89134ec2e8>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: RFE.X.YeoJohnson.glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
rsp_var_out <- paste0(glb_rsp_var, ".predict.")
predct_var_name <- paste0(rsp_var_out, mdl_id)
predct_accurate_var_name <- paste0(rsp_var_out, mdl_id, ".accurate")
predct_error_var_name <- paste0(rsp_var_out, mdl_id, ".err")
predct_erabs_var_name <- paste0(rsp_var_out, mdl_id, ".err.abs")
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df; glb_trnobs_df <- sav_trnobs_df; glb_fitobs_df <- sav_fitobs_df; glb_OOBobs_df <- sav_OOBobs_df; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- paste0(glb_rsp_var_out, mdl_id)
predct_error_var_name <- paste0(glb_rsp_var_out, mdl_id, ".err.abs")
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var_out)
tmp_obs_df <- obs_df %>%
dplyr::select_(glb_category_var, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glb_category_var) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glb_category_var,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glb_category_var, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
if (glb_mdl_ensemble == "auto") {
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
# mdl_threshold_pos <- min(which(tmp_models_df$id %in%
# c("MFO.myMFO_classfr", "Baseline.mybaseln_classfr"))) - 1
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
glb_mdl_ensemble <- tmp_models_df$id[1:mdl_threshold_pos]
}
for (mdl_id in glb_mdl_ensemble) {
glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id, glb_rsp_var_out)
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id, glb_rsp_var_out)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(glb_rsp_var_out, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glb_fitobs_df[, glb_rsp_var], glb_fitobs_df[, paste(glb_rsp_var_out, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, "Ensemble.glmnet", glb_rsp_var_out);print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(glb_rsp_var_out, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# indep_vars <- grep(glb_rsp_var_out, names(glb_fitobs_df), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glb_fitobs_df, indep_vars)
for (method in c("glmnet")) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type="regression", tune.df=NULL,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method)),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}
dsp_models_df <- get_dsp_models_df()
}## Warning in if (glb_mdl_ensemble == "auto") {: the condition has length > 1
## and only the first element will be used## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] "fitting model: Ensemble.glmnet"
## [1] " indep_vars: startprice.log10.predict.RFE.X.glm,startprice.log10.predict.RFE.X.glmnet,startprice.log10.predict.RFE.X.rpart,startprice.log10.predict.RFE.X.gbm,startprice.log10.predict.RFE.X.rf,startprice.log10.predict.RFE.X.svmLinear,startprice.log10.predict.RFE.X.svmLinear2,startprice.log10.predict.RFE.X.earth,startprice.log10.predict.RFE.X.bagEarth,startprice.log10.predict.RFE.X.Interact.glmnet,startprice.log10.predict.RFE.X.YeoJohnson.glmnet,startprice.log10.predict.RFE.X.center.scale.glmnet,startprice.log10.predict.RFE.X.spatialSign.glmnet"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.000254 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 88 -none- numeric
## beta 1144 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 13 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.05324837
## startprice.log10.predict.RFE.X.Interact.glmnet
## 0.37935987
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## -0.04596983
## startprice.log10.predict.RFE.X.earth
## -0.27953899
## startprice.log10.predict.RFE.X.gbm
## 0.01680418
## startprice.log10.predict.RFE.X.glm
## 0.17130529
## startprice.log10.predict.RFE.X.glmnet
## -0.11723211
## startprice.log10.predict.RFE.X.rf
## 1.22740411
## startprice.log10.predict.RFE.X.rpart
## -0.11782406
## startprice.log10.predict.RFE.X.svmLinear
## -0.20921150
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.0528981580
## startprice.log10.predict.RFE.X.Interact.glmnet
## 0.3812295543
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## -0.0481972796
## startprice.log10.predict.RFE.X.bagEarth
## 0.0004525666
## startprice.log10.predict.RFE.X.earth
## -0.2800315018
## startprice.log10.predict.RFE.X.gbm
## 0.0177880147
## startprice.log10.predict.RFE.X.glm
## 0.1746779001
## startprice.log10.predict.RFE.X.glmnet
## -0.1207250233
## startprice.log10.predict.RFE.X.rf
## 1.2284592991
## startprice.log10.predict.RFE.X.rpart
## -0.1177636331
## startprice.log10.predict.RFE.X.svmLinear
## -0.2109445418
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Ensemble.glmnet
## feats
## 1 startprice.log10.predict.RFE.X.glm,startprice.log10.predict.RFE.X.glmnet,startprice.log10.predict.RFE.X.rpart,startprice.log10.predict.RFE.X.gbm,startprice.log10.predict.RFE.X.rf,startprice.log10.predict.RFE.X.svmLinear,startprice.log10.predict.RFE.X.svmLinear2,startprice.log10.predict.RFE.X.earth,startprice.log10.predict.RFE.X.bagEarth,startprice.log10.predict.RFE.X.Interact.glmnet,startprice.log10.predict.RFE.X.YeoJohnson.glmnet,startprice.log10.predict.RFE.X.center.scale.glmnet,startprice.log10.predict.RFE.X.spatialSign.glmnet
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.92 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9660964 0.05431488 0.9637766 0.2137137 0.2884323
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2053283 0.9616063 0.009138167 0.01565616
## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.lm Max.cor.Y.rcv.1X1.glmnet
## 0 0
## Max.cor.Y.rcv.1X1.cp.0.rpart RFE.X.bagEarth
## 0 1if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 88 -none- numeric
## beta 1144 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 13 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.05324837
## startprice.log10.predict.RFE.X.Interact.glmnet
## 0.37935987
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## -0.04596983
## startprice.log10.predict.RFE.X.earth
## -0.27953899
## startprice.log10.predict.RFE.X.gbm
## 0.01680418
## startprice.log10.predict.RFE.X.glm
## 0.17130529
## startprice.log10.predict.RFE.X.glmnet
## -0.11723211
## startprice.log10.predict.RFE.X.rf
## 1.22740411
## startprice.log10.predict.RFE.X.rpart
## -0.11782406
## startprice.log10.predict.RFE.X.svmLinear
## -0.20921150
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.0528981580
## startprice.log10.predict.RFE.X.Interact.glmnet
## 0.3812295543
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## -0.0481972796
## startprice.log10.predict.RFE.X.bagEarth
## 0.0004525666
## startprice.log10.predict.RFE.X.earth
## -0.2800315018
## startprice.log10.predict.RFE.X.gbm
## 0.0177880147
## startprice.log10.predict.RFE.X.glm
## 0.1746779001
## startprice.log10.predict.RFE.X.glmnet
## -0.1207250233
## startprice.log10.predict.RFE.X.rf
## 1.2284592991
## startprice.log10.predict.RFE.X.rpart
## -0.1177636331
## startprice.log10.predict.RFE.X.svmLinear
## -0.2109445418## [1] TRUE#stop(here"); glb_to_sav()
# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet fit prediction diagnostics:"glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet OOB prediction diagnostics:"glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".importance")] <- glb_featsimp_df$importance; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.importance) | (abs(RFE.X.YeoJohnson.glmnet.importance - RFE.X.glmnet.importance) > 0.0001), select=-importance)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)## importance
## startprice.log10.predict.RFE.X.rf 100.000000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.778661
## startprice.log10.predict.RFE.X.glm 30.027726
## startprice.log10.predict.RFE.X.gbm 19.703054
## startprice.log10.predict.RFE.X.bagEarth 18.571329
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.556706
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.556706
## startprice.log10.predict.RFE.X.svmLinear2 18.556706
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.435727
## startprice.log10.predict.RFE.X.rpart 10.743822
## startprice.log10.predict.RFE.X.glmnet 10.668275
## startprice.log10.predict.RFE.X.svmLinear 4.624482
## startprice.log10.predict.RFE.X.earth 0.000000
## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.rf 100.000000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.778661
## startprice.log10.predict.RFE.X.glm 30.027726
## startprice.log10.predict.RFE.X.gbm 19.703054
## startprice.log10.predict.RFE.X.bagEarth 18.571329
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.556706
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.556706
## startprice.log10.predict.RFE.X.svmLinear2 18.556706
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.435727
## startprice.log10.predict.RFE.X.rpart 10.743822
## startprice.log10.predict.RFE.X.glmnet 10.668275
## startprice.log10.predict.RFE.X.svmLinear 4.624482
## startprice.log10.predict.RFE.X.earth 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(importance.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 13
## UniqueID description biddable startprice condition cellular carrier
## 866 11591 0 2.99 New Unknown Unknown
## 745 11444 0 3.99 New Unknown Unknown
## 749 11448 0 4.69 New Unknown Unknown
## 858 11583 0 4.99 New Unknown Unknown
## 1329 12479 0 5.65 New Unknown Unknown
## color storage productline sold .src .grpid startprice.log10
## 866 Unknown Unknown Unknown 0 Test <NA> 0.4756712
## 745 Unknown Unknown Unknown 0 Test <NA> 0.6009729
## 749 Unknown Unknown Unknown 1 Train <NA> 0.6711728
## 858 Unknown Unknown Unknown 1 Train <NA> 0.6981005
## 1329 Unknown Unknown Unknown NA Test <NA> 0.7520484
## .rnorm startprice.log10.cut.fctr descr.my condition.fctr
## 866 0.23582653 (0.473,1.32] New
## 745 0.08115265 (0.473,1.32] New
## 749 -0.03918280 (0.473,1.32] New
## 858 -1.89238602 (0.473,1.32] New
## 1329 -2.22233585 (0.473,1.32] New
## cellular.fctr carrier.fctr color.fctr storage.fctr
## 866 Unknown Unknown Unknown Unknown
## 745 Unknown Unknown Unknown Unknown
## 749 Unknown Unknown Unknown Unknown
## 858 Unknown Unknown Unknown Unknown
## 1329 Unknown Unknown Unknown Unknown
## D.terms.post.stop.n D.terms.post.stop.n.log D.weight.post.stop.sum
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.terms.post.stem.n D.terms.post.stem.n.log D.weight.post.stem.sum
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.terms.n.stem.stop.Ratio D.weight.sum.stem.stop.Ratio D.T.condit
## 866 1 1 0
## 745 1 1 0
## 749 1 1 0
## 858 1 1 0
## 1329 1 1 0
## D.T.use D.T.new D.T.scratch D.T.good D.T.work D.T.screen D.T.great
## 866 0 0 0 0 0 0 0
## 745 0 0 0 0 0 0 0
## 749 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0
## 1329 0 0 0 0 0 0 0
## D.T.box D.T.no D.T.excel D.T.like D.T.item D.T.cosmet D.T.minor
## 866 0 0 0 0 0 0 0
## 745 0 0 0 0 0 0 0
## 749 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0
## 1329 0 0 0 0 0 0 0
## D.T.crack D.T.perfect D.T.fulli D.T.function. D.T.hous D.T.origin
## 866 0 0 0 0 0 0
## 745 0 0 0 0 0 0
## 749 0 0 0 0 0 0
## 858 0 0 0 0 0 0
## 1329 0 0 0 0 0 0
## D.T.will D.T.X100 D.T.damag D.T.open D.T.tab D.T.list D.T.affect
## 866 0 0 0 0 0 0 0
## 745 0 0 0 0 0 0 0
## 749 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0
## 1329 0 0 0 0 0 0 0
## D.wrds.n.log D.wrds.unq.n.log D.weight.sum D.ratio.weight.sum.wrds.n
## 866 0 0 0 0
## 745 0 0 0 0
## 749 0 0 0 0
## 858 0 0 0 0
## 1329 0 0 0 0
## D.chrs.n.log D.chrs.uppr.n.log D.dgts.n.log D.chrs.pnct01.n.log
## 866 0 0 0 0
## 745 0 0 0 0
## 749 0 0 0 0
## 858 0 0 0 0
## 1329 0 0 0 0
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log D.chrs.pnct13.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log D.wrds.stop.n.log
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## D.ratio.wrds.stop.n.wrds.n D.P.http D.P.mini D.P.air D.P.black
## 866 1 0 0 0 0
## 745 1 0 0 0 0
## 749 1 0 0 0 0
## 858 1 0 0 0 0
## 1329 1 0 0 0 0
## D.P.white D.P.gold D.P.spacegray prdl.descr.my.fctr
## 866 0 0 0 Unknown#0
## 745 0 0 0 Unknown#0
## 749 0 0 0 Unknown#0
## 858 0 0 0 Unknown#0
## 1329 0 0 0 Unknown#0
## startprice.dgt1.is9 startprice.dgt2.is9 startprice.dgt3.is9
## 866 0 0 0
## 745 0 0 0
## 749 0 0 0
## 858 0 0 0
## 1329 0 0 0
## startprice.dcm1.is9 startprice.dcm2.is9 .clusterid .clusterid.fctr
## 866 1 1 1 1
## 745 1 1 1 1
## 749 0 1 1 1
## 858 1 1 1 1
## 1329 0 0 1 1
## startprice.log10.predict.RFE.X.glm
## 866 2.534750
## 745 2.534485
## 749 2.570859
## 858 2.531106
## 1329 2.547161
## startprice.log10.predict.RFE.X.glm.err
## 866 2.059079
## 745 1.933512
## 749 1.899686
## 858 1.833005
## 1329 1.795112
## startprice.log10.predict.RFE.X.glm.err.abs
## 866 2.059079
## 745 1.933512
## 749 1.899686
## 858 1.833005
## 1329 1.795112
## startprice.log10.predict.RFE.X.glm.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 866 2.569054
## 745 2.569054
## 749 2.569101
## 858 2.569054
## 1329 2.569101
## startprice.log10.predict.RFE.X.glmnet.err
## 866 2.093383
## 745 1.968081
## 749 1.897929
## 858 1.870953
## 1329 1.817053
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 866 2.093383
## 745 1.968081
## 749 1.897929
## 858 1.870953
## 1329 1.817053
## startprice.log10.predict.RFE.X.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.rpart
## 866 2.594138
## 745 2.594138
## 749 2.594138
## 858 2.594138
## 1329 2.594138
## startprice.log10.predict.RFE.X.rpart.err
## 866 2.118467
## 745 1.993165
## 749 1.922965
## 858 1.896038
## 1329 1.842090
## startprice.log10.predict.RFE.X.rpart.err.abs
## 866 2.118467
## 745 1.993165
## 749 1.922965
## 858 1.896038
## 1329 1.842090
## startprice.log10.predict.RFE.X.rpart.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.gbm
## 866 2.660779
## 745 2.660779
## 749 2.658117
## 858 2.660779
## 1329 2.653162
## startprice.log10.predict.RFE.X.gbm.err
## 866 2.185108
## 745 2.059806
## 749 1.986944
## 858 1.962678
## 1329 1.901113
## startprice.log10.predict.RFE.X.gbm.err.abs
## 866 2.185108
## 745 2.059806
## 749 1.986944
## 858 1.962678
## 1329 1.901113
## startprice.log10.predict.RFE.X.gbm.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.rf
## 866 2.709043
## 745 2.709043
## 749 2.709361
## 858 2.709043
## 1329 2.710873
## startprice.log10.predict.RFE.X.rf.err
## 866 2.233372
## 745 2.108071
## 749 2.038188
## 858 2.010943
## 1329 1.958825
## startprice.log10.predict.RFE.X.rf.err.abs
## 866 2.233372
## 745 2.108071
## 749 2.038188
## 858 2.010943
## 1329 1.958825
## startprice.log10.predict.RFE.X.rf.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 866 2.554886
## 745 2.554886
## 749 2.569670
## 858 2.554886
## 1329 2.552102
## startprice.log10.predict.RFE.X.svmLinear.err
## 866 2.079215
## 745 1.953913
## 749 1.898497
## 858 1.856785
## 1329 1.800053
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 866 2.079215
## 745 1.953913
## 749 1.898497
## 858 1.856785
## 1329 1.800053
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 866 2.555239
## 745 2.555239
## 749 2.583969
## 858 2.555239
## 1329 2.558916
## startprice.log10.predict.RFE.X.svmLinear2.err
## 866 2.079568
## 745 1.954266
## 749 1.912796
## 858 1.857138
## 1329 1.806868
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 866 2.079568
## 745 1.954266
## 749 1.912796
## 858 1.857138
## 1329 1.806868
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.earth
## 866 2.611742
## 745 2.611742
## 749 2.611742
## 858 2.611742
## 1329 2.611742
## startprice.log10.predict.RFE.X.earth.err
## 866 2.136071
## 745 2.010769
## 749 1.940569
## 858 1.913641
## 1329 1.859694
## startprice.log10.predict.RFE.X.earth.err.abs
## 866 2.136071
## 745 2.010769
## 749 1.940569
## 858 1.913641
## 1329 1.859694
## startprice.log10.predict.RFE.X.earth.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 866 2.576492
## 745 2.576492
## 749 2.581268
## 858 2.576492
## 1329 2.587724
## startprice.log10.predict.RFE.X.bagEarth.err
## 866 2.100821
## 745 1.975519
## 749 1.910095
## 858 1.878392
## 1329 1.835676
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 866 2.100821
## 745 1.975519
## 749 1.910095
## 858 1.878392
## 1329 1.835676
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.Interact.glmnet
## 866 2.549609
## 745 2.549609
## 749 2.549609
## 858 2.549609
## 1329 2.549609
## startprice.log10.predict.RFE.X.Interact.glmnet.err
## 866 2.073937
## 745 1.948636
## 749 1.878436
## 858 1.851508
## 1329 1.797560
## startprice.log10.predict.RFE.X.Interact.glmnet.err.abs
## 866 2.073937
## 745 1.948636
## 749 1.878436
## 858 1.851508
## 1329 1.797560
## startprice.log10.predict.RFE.X.Interact.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## 866 2.5691
## 745 2.5691
## 749 2.5691
## 858 2.5691
## 1329 2.5691
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err
## 866 2.093429
## 745 1.968127
## 749 1.897927
## 858 1.871000
## 1329 1.817052
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err.abs
## 866 2.093429
## 745 1.968127
## 749 1.897927
## 858 1.871000
## 1329 1.817052
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.center.scale.glmnet
## 866 2.569054
## 745 2.569054
## 749 2.569101
## 858 2.569054
## 1329 2.569101
## startprice.log10.predict.RFE.X.center.scale.glmnet.err
## 866 2.093383
## 745 1.968081
## 749 1.897929
## 858 1.870953
## 1329 1.817053
## startprice.log10.predict.RFE.X.center.scale.glmnet.err.abs
## 866 2.093383
## 745 1.968081
## 749 1.897929
## 858 1.870953
## 1329 1.817053
## startprice.log10.predict.RFE.X.center.scale.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.RFE.X.spatialSign.glmnet
## 866 2.554010
## 745 2.554141
## 749 2.557881
## 858 2.545282
## 1329 2.550995
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err
## 866 2.078339
## 745 1.953168
## 749 1.886708
## 858 1.847181
## 1329 1.798947
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err.abs
## 866 2.078339
## 745 1.953168
## 749 1.886708
## 858 1.847181
## 1329 1.798947
## startprice.log10.predict.RFE.X.spatialSign.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 749 FALSE
## 858 FALSE
## 1329 FALSE
## startprice.log10.predict.Ensemble.glmnet
## 866 2.728493
## 745 2.728447
## 749 2.731972
## 858 2.727863
## 1329 2.733336
## startprice.log10.predict.Ensemble.glmnet.err
## 866 2.252822
## 745 2.127474
## 749 2.060799
## 858 2.029762
## 1329 1.981288
## startprice.log10.predict.Ensemble.glmnet.err.abs
## 866 2.252822
## 745 2.127474
## 749 2.060799
## 858 2.029762
## 1329 1.981288
## startprice.log10.predict.Ensemble.glmnet.accurate .label
## 866 FALSE 11591
## 745 FALSE 11444
## 749 FALSE 11448
## 858 FALSE 11583
## 1329 FALSE 12479
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id, label="OOB"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glb_model_evl_criteria)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))## prdl.descr.my.fctr .n.Tst .n.OOB.x .freqRatio.Tst
## Unknown#0 Unknown#0 83 86 0.068032787
## Unknown#1 Unknown#1 65 66 0.053278689
## iPadmini#1 iPadmini#1 63 64 0.051639344
## iPad1#1 iPad1#1 59 61 0.048360656
## iPad1#0 iPad1#0 57 57 0.046721311
## iPadAir2#1 iPadAir2#1 29 30 0.023770492
## iPadmini2#1 iPadmini2#1 24 24 0.019672131
## iPadAir2#0 iPadAir2#0 92 92 0.075409836
## iPadmini2#0 iPadmini2#0 44 44 0.036065574
## iPadmini#0 iPadmini#0 97 97 0.079508197
## iPadmini3#1 iPadmini3#1 12 12 0.009836066
## iPad2#0 iPad2#0 72 73 0.059016393
## iPadmini3#0 iPadmini3#0 63 63 0.051639344
## iPad3#1 iPad3#1 63 64 0.051639344
## iPad4#1 iPad4#1 74 76 0.060655738
## iPadAir#0 iPadAir#0 60 60 0.049180328
## iPad4#0 iPad4#0 42 42 0.034426230
## iPad2#1 iPad2#1 126 126 0.103278689
## iPadAir#1 iPadAir#1 67 68 0.054918033
## iPad3#0 iPad3#0 28 28 0.022950820
## .freqRatio.OOB err.abs.fit.sum err.abs.fit.mean .n.fit
## Unknown#0 0.06974858 0.97148291 0.08095691 12
## Unknown#1 0.05352798 0.64173657 0.06417366 10
## iPadmini#1 0.05190592 0.61520329 0.05126694 12
## iPad1#1 0.04947283 0.63882356 0.04914027 13
## iPad1#0 0.04622871 0.59144797 0.04224628 14
## iPadAir2#1 0.02433090 0.15612627 0.03903157 4
## iPadmini2#1 0.01946472 0.14258652 0.03564663 4
## iPadAir2#0 0.07461476 0.52415447 0.03494363 15
## iPadmini2#0 0.03568532 0.38178240 0.03470749 11
## iPadmini#0 0.07866991 0.64386457 0.03219323 20
## iPadmini3#1 0.00973236 0.03093406 0.03093406 1
## iPad2#0 0.05920519 0.36039655 0.03003305 12
## iPadmini3#0 0.05109489 0.23487994 0.02935999 8
## iPad3#1 0.05190592 0.37686393 0.02898953 13
## iPad4#1 0.06163828 0.16843758 0.02807293 6
## iPadAir#0 0.04866180 0.33307290 0.02775607 12
## iPad4#0 0.03406326 0.21763133 0.01978467 11
## iPad2#1 0.10218978 0.30401382 0.01600073 19
## iPadAir#1 0.05515004 0.05829557 0.01457389 4
## iPad3#0 0.02270884 0.04210759 0.01403586 3
## err.abs.OOB.sum err.abs.OOB.mean .n.OOB.y
## Unknown#0 43.540313 0.50628271 86
## Unknown#1 16.541495 0.25062871 66
## iPadmini#1 7.657773 0.11965270 64
## iPad1#1 6.697294 0.10979170 61
## iPad1#0 7.642822 0.13408460 57
## iPadAir2#1 8.173194 0.27243980 30
## iPadmini2#1 2.674542 0.11143926 24
## iPadAir2#0 8.687314 0.09442733 92
## iPadmini2#0 4.568395 0.10382717 44
## iPadmini#0 12.842770 0.13239970 97
## iPadmini3#1 2.822667 0.23522228 12
## iPad2#0 11.101121 0.15207015 73
## iPadmini3#0 5.142928 0.08163377 63
## iPad3#1 6.225250 0.09726953 64
## iPad4#1 13.401229 0.17633196 76
## iPadAir#0 7.556450 0.12594083 60
## iPad4#0 5.826083 0.13871626 42
## iPad2#1 15.134406 0.12011434 126
## iPadAir#1 9.577808 0.14085012 68
## iPad3#0 5.081787 0.18149239 28print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))## .n.Tst .n.OOB.x .freqRatio.Tst .freqRatio.OOB
## 1220.0000000 1233.0000000 1.0000000 1.0000000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 7.4338418 0.7038474 204.0000000 200.8956429
## err.abs.OOB.mean .n.OOB.y
## 3.2846153 1233.0000000write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 12 fit.models 7 2 2 299.584 351.635 52.051
## 13 fit.models 7 3 3 351.636 NA NA# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb_to_sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glb_fitobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
for (col in setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
if (all(is.na(glb_newobs_df[, glb_rsp_var])))
for (col in setdiff(names(glb_OOBobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
}
sync_glb_obs_df()
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 13 fit.models 7 3 3 351.636 358.248
## 14 fit.data.training 8 0 0 358.249 NA
## elapsed
## 13 6.612
## 14 NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else if (nrow(glb_fitobs_df) + length(glb_obsfit_outliers) == nrow(glb_trnobs_df)) {
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE", glb_sel_mdl_id) || grepl("RFE", glb_mdl_ensemble))
stop("RFE indep_vars should be based on glb_trnobs_df. Also, outliers in OOB.\nNot implemented yet")
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
# Fit selected models on glb_trnobs_df
for (mdl_id in gsub(".prob", "",
gsub(glb_rsp_var_out, "", row.names(mdlimp_df), fixed=TRUE),
fixed=TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
ret_lst <-
myfit_mdl(mdl_id=paste0(c(head(mdl_id_components, -1), "Train"),
collapse="."),
model_method=tail(mdl_id_components, 1),
indep_vars_vctr=trim(unlist(strsplit(
glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df,
mdl_id=tail(glb_models_df$id, 1),
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=subset(glb_models_df,
mdl_id == mdl_id)$opt.prob.threshold.OOB)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df,
mdl_id=tail(glb_models_df$id, 1),
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=subset(glb_models_df,
mdl_id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
# Discontinuing use of tune_finmdl_df;
# since final model needs to be cved on glb_trnobs_df
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.", paste(glb_preproc_methods, collapse="|"),
fixed=TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
fit_trnobs_df <- if (is.null(glb_obstrn_outliers)) glb_trnobs_df else
glb_trnobs_df[!(glb_trnobs_df[, glb_id_var] %in% glb_obstrn_outliers), ]
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c("glm", tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)))
for (method in method_vctr) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=ifelse(grepl("Ensemble", glb_sel_mdl_id), "Final.Ensemble", "Final"),
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method,
train.preProcess=ths_preProcess)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fit_trnobs_df, OOB_df=NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## Warning: Final model same as glb_sel_mdl_idrm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 14 fit.data.training 8 0 0 358.249 358.713
## 15 fit.data.training 8 1 1 358.713 NA
## elapsed
## 14 0.464
## 15 NA#stop(here"); glb_to_sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
for (mdl_id in glb_mdl_ensemble) {
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
}
}## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.rf 100.000000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.778661
## startprice.log10.predict.RFE.X.glm 30.027726
## startprice.log10.predict.RFE.X.gbm 19.703054
## startprice.log10.predict.RFE.X.bagEarth 18.571329
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.556706
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.556706
## startprice.log10.predict.RFE.X.svmLinear2 18.556706
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.435727
## startprice.log10.predict.RFE.X.rpart 10.743822
## startprice.log10.predict.RFE.X.glmnet 10.668275
## startprice.log10.predict.RFE.X.svmLinear 4.624482
## startprice.log10.predict.RFE.X.earth 0.000000
## importance
## startprice.log10.predict.RFE.X.rf 100.000000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.778661
## startprice.log10.predict.RFE.X.glm 30.027726
## startprice.log10.predict.RFE.X.gbm 19.703054
## startprice.log10.predict.RFE.X.bagEarth 18.571329
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.556706
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.556706
## startprice.log10.predict.RFE.X.svmLinear2 18.556706
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.435727
## startprice.log10.predict.RFE.X.rpart 10.743822
## startprice.log10.predict.RFE.X.glmnet 10.668275
## startprice.log10.predict.RFE.X.svmLinear 4.624482
## startprice.log10.predict.RFE.X.earth 0.000000
## Final.Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.rf 100.000000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.778661
## startprice.log10.predict.RFE.X.glm 30.027726
## startprice.log10.predict.RFE.X.gbm 19.703054
## startprice.log10.predict.RFE.X.bagEarth 18.571329
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.556706
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.556706
## startprice.log10.predict.RFE.X.svmLinear2 18.556706
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.435727
## startprice.log10.predict.RFE.X.rpart 10.743822
## startprice.log10.predict.RFE.X.glmnet 10.668275
## startprice.log10.predict.RFE.X.svmLinear 4.624482
## startprice.log10.predict.RFE.X.earth 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 13
## UniqueID description startprice
## 749 11448 4.69
## 858 11583 4.99
## 856 11581 Works great! 8.00
## 336 10837 37.98
## 743 11442 Open box, was on exhibition, never used before. 44.99
## condition cellular carrier color storage productline
## 749 New Unknown Unknown Unknown Unknown Unknown
## 858 New Unknown Unknown Unknown Unknown Unknown
## 856 Used Unknown Unknown Unknown Unknown iPad 4
## 336 Used Unknown Unknown Unknown Unknown Unknown
## 743 New other (see details) Unknown Unknown Unknown Unknown Unknown
## .src .grpid startprice.log10 .rnorm startprice.log10.cut.fctr
## 749 Train <NA> 0.6711728 -0.0391828 (0.473,1.32]
## 858 Train <NA> 0.6981005 -1.8923860 (0.473,1.32]
## 856 Train <NA> 0.9030900 -0.5963850 (0.473,1.32]
## 336 Train <NA> 1.5795550 1.8959529 (1.32,2.16]
## 743 Train <NA> 1.6531160 0.3147328 (1.32,2.16]
## descr.my
## 749
## 858
## 856 Works great!
## 336
## 743 Open box, was on exhibition, never used before.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 749 New Unknown Unknown Unknown
## 858 New Unknown Unknown Unknown
## 856 Used Unknown Unknown Unknown
## 336 Used Unknown Unknown Unknown
## 743 New other (see details) Unknown Unknown Unknown
## storage.fctr D.terms.post.stop.n D.terms.post.stop.n.log
## 749 Unknown 0 0.000000
## 858 Unknown 0 0.000000
## 856 Unknown 2 1.098612
## 336 Unknown 0 0.000000
## 743 Unknown 4 1.609438
## D.weight.post.stop.sum D.terms.post.stem.n D.terms.post.stem.n.log
## 749 0.000000 0 0.000000
## 858 0.000000 0 0.000000
## 856 4.870239 2 1.098612
## 336 0.000000 0 0.000000
## 743 5.368994 4 1.609438
## D.weight.post.stem.sum D.weight.sum.stem.stop.Ratio D.T.condit
## 749 0.000000 1.0000000 0
## 858 0.000000 1.0000000 0
## 856 3.936873 0.8083530 0
## 336 0.000000 1.0000000 0
## 743 5.303662 0.9878316 0
## D.T.use D.T.scratch D.T.screen D.wrds.n.log D.wrds.unq.n.log
## 749 0.0000000 0 0 0.000000 0.000000
## 858 0.0000000 0 0 0.000000 0.000000
## 856 0.0000000 0 0 1.098612 1.098612
## 336 0.0000000 0 0 0.000000 0.000000
## 743 0.7719912 0 0 2.197225 1.609438
## D.weight.sum D.ratio.weight.sum.wrds.n D.chrs.n.log D.chrs.uppr.n.log
## 749 0.000000 0.0000000 0.000000 0.000000
## 858 0.000000 0.0000000 0.000000 0.000000
## 856 3.936873 1.9684363 2.564949 2.397895
## 336 0.000000 0.0000000 0.000000 0.000000
## 743 5.303662 0.6629577 3.871201 3.637586
## D.chrs.pnct11.n.log D.chrs.pnct13.n.log D.wrds.stop.n.log
## 749 0.000000 0.0000000 0.000000
## 858 0.000000 0.0000000 0.000000
## 856 0.000000 0.0000000 0.000000
## 336 0.000000 0.0000000 0.000000
## 743 1.098612 0.6931472 1.386294
## D.ratio.wrds.stop.n.wrds.n prdl.descr.my.fctr startprice.dgt1.is9
## 749 1.0000000 Unknown#0 0
## 858 1.0000000 Unknown#0 0
## 856 0.3333333 iPad4#1 0
## 336 1.0000000 Unknown#0 0
## 743 0.4444444 Unknown#1 0
## startprice.dgt2.is9 startprice.dcm1.is9 startprice.dcm2.is9 .clusterid
## 749 0 0 1 1
## 858 0 1 1 1
## 856 0 0 0 3
## 336 0 1 0 1
## 743 0 1 1 1
## .clusterid.fctr .lcn sold biddable D.terms.n.stem.stop.Ratio D.T.new
## 749 1 OOB 1 NA NA NA
## 858 1 OOB 1 NA NA NA
## 856 3 OOB 1 NA NA NA
## 336 1 OOB 1 NA NA NA
## 743 1 OOB 1 NA NA NA
## D.T.good D.T.work D.T.great D.T.box D.T.no D.T.excel D.T.like D.T.item
## 749 NA NA NA NA NA NA NA NA
## 858 NA NA NA NA NA NA NA NA
## 856 NA NA NA NA NA NA NA NA
## 336 NA NA NA NA NA NA NA NA
## 743 NA NA NA NA NA NA NA NA
## D.T.cosmet D.T.minor D.T.crack D.T.perfect D.T.fulli D.T.function.
## 749 NA NA NA NA NA NA
## 858 NA NA NA NA NA NA
## 856 NA NA NA NA NA NA
## 336 NA NA NA NA NA NA
## 743 NA NA NA NA NA NA
## D.T.hous D.T.origin D.T.will D.T.X100 D.T.damag D.T.open D.T.tab
## 749 NA NA NA NA NA NA NA
## 858 NA NA NA NA NA NA NA
## 856 NA NA NA NA NA NA NA
## 336 NA NA NA NA NA NA NA
## 743 NA NA NA NA NA NA NA
## D.T.list D.T.affect D.dgts.n.log D.chrs.pnct01.n.log
## 749 NA NA NA NA
## 858 NA NA NA NA
## 856 NA NA NA NA
## 336 NA NA NA NA
## 743 NA NA NA NA
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct12.n.log D.chrs.pnct14.n.log D.chrs.pnct15.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct16.n.log D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log D.chrs.pnct21.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct22.n.log D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log D.chrs.pnct27.n.log
## 749 NA NA NA
## 858 NA NA NA
## 856 NA NA NA
## 336 NA NA NA
## 743 NA NA NA
## D.chrs.pnct28.n.log D.chrs.pnct29.n.log D.chrs.pnct30.n.log D.P.http
## 749 NA NA NA NA
## 858 NA NA NA NA
## 856 NA NA NA NA
## 336 NA NA NA NA
## 743 NA NA NA NA
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 749 NA NA NA NA NA NA
## 858 NA NA NA NA NA NA
## 856 NA NA NA NA NA NA
## 336 NA NA NA NA NA NA
## 743 NA NA NA NA NA NA
## startprice.dgt3.is9 startprice.log10.predict.RFE.X.glm
## 749 NA 2.570859
## 858 NA 2.531106
## 856 NA 2.520053
## 336 NA 2.362585
## 743 NA 2.505452
## startprice.log10.predict.RFE.X.glm.err
## 749 1.8996862
## 858 1.8330052
## 856 1.6169634
## 336 0.7830298
## 743 0.8523357
## startprice.log10.predict.RFE.X.glm.err.abs
## 749 1.8996862
## 858 1.8330052
## 856 1.6169634
## 336 0.7830298
## 743 0.8523357
## startprice.log10.predict.RFE.X.glm.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 749 2.569101
## 858 2.569054
## 856 2.495270
## 336 2.412139
## 743 2.480923
## startprice.log10.predict.RFE.X.glmnet.err
## 749 1.8979286
## 858 1.8709533
## 856 1.5921797
## 336 0.8325845
## 743 0.8278068
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 749 1.8979286
## 858 1.8709533
## 856 1.5921797
## 336 0.8325845
## 743 0.8278068
## startprice.log10.predict.RFE.X.glmnet.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.rpart
## 749 2.594138
## 858 2.594138
## 856 2.426605
## 336 2.426605
## 743 2.426605
## startprice.log10.predict.RFE.X.rpart.err
## 749 1.9229655
## 858 1.8960378
## 856 1.5235151
## 336 0.8470501
## 743 0.7734891
## startprice.log10.predict.RFE.X.rpart.err.abs
## 749 1.9229655
## 858 1.8960378
## 856 1.5235151
## 336 0.8470501
## 743 0.7734891
## startprice.log10.predict.RFE.X.rpart.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.gbm
## 749 2.658117
## 858 2.660779
## 856 2.457951
## 336 2.459438
## 743 2.420094
## startprice.log10.predict.RFE.X.gbm.err
## 749 1.986944
## 858 1.962678
## 856 1.554861
## 336 0.879883
## 743 0.766978
## startprice.log10.predict.RFE.X.gbm.err.abs
## 749 1.986944
## 858 1.962678
## 856 1.554861
## 336 0.879883
## 743 0.766978
## startprice.log10.predict.RFE.X.gbm.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.rf
## 749 2.709361
## 858 2.709043
## 856 2.364765
## 336 2.374940
## 743 2.374530
## startprice.log10.predict.RFE.X.rf.err
## 749 2.0381883
## 858 2.0109429
## 856 1.4616750
## 336 0.7953854
## 743 0.7214140
## startprice.log10.predict.RFE.X.rf.err.abs
## 749 2.0381883
## 858 2.0109429
## 856 1.4616750
## 336 0.7953854
## 743 0.7214140
## startprice.log10.predict.RFE.X.rf.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 749 2.569670
## 858 2.554886
## 856 2.513974
## 336 2.371798
## 743 2.476604
## startprice.log10.predict.RFE.X.svmLinear.err
## 749 1.8984972
## 858 1.8567854
## 856 1.6108842
## 336 0.7922429
## 743 0.8234885
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 749 1.8984972
## 858 1.8567854
## 856 1.6108842
## 336 0.7922429
## 743 0.8234885
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 749 2.583969
## 858 2.555239
## 856 2.496869
## 336 2.349823
## 743 2.422571
## startprice.log10.predict.RFE.X.svmLinear2.err
## 749 1.9127957
## 858 1.8571383
## 856 1.5937790
## 336 0.7702683
## 743 0.7694552
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 749 1.9127957
## 858 1.8571383
## 856 1.5937790
## 336 0.7702683
## 743 0.7694552
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.earth
## 749 2.611742
## 858 2.611742
## 856 2.394962
## 336 2.394962
## 743 2.394962
## startprice.log10.predict.RFE.X.earth.err
## 749 1.9405691
## 858 1.9136414
## 856 1.4918724
## 336 0.8154075
## 743 0.7418464
## startprice.log10.predict.RFE.X.earth.err.abs
## 749 1.9405691
## 858 1.9136414
## 856 1.4918724
## 336 0.8154075
## 743 0.7418464
## startprice.log10.predict.RFE.X.earth.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 749 2.581268
## 858 2.576492
## 856 2.498894
## 336 2.426060
## 743 2.449793
## startprice.log10.predict.RFE.X.bagEarth.err
## 749 1.9100955
## 858 1.8783917
## 856 1.5958037
## 336 0.8465054
## 743 0.7966766
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 749 1.9100955
## 858 1.8783917
## 856 1.5958037
## 336 0.8465054
## 743 0.7966766
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.Interact.glmnet
## 749 2.549609
## 858 2.549609
## 856 2.416862
## 336 2.388709
## 743 2.442430
## startprice.log10.predict.RFE.X.Interact.glmnet.err
## 749 1.8784357
## 858 1.8515080
## 856 1.5137722
## 336 0.8091537
## 743 0.7893145
## startprice.log10.predict.RFE.X.Interact.glmnet.err.abs
## 749 1.8784357
## 858 1.8515080
## 856 1.5137722
## 336 0.8091537
## 743 0.7893145
## startprice.log10.predict.RFE.X.Interact.glmnet.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## 749 2.569100
## 858 2.569100
## 856 2.520927
## 336 2.412223
## 743 2.487144
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err
## 749 1.8979274
## 858 1.8709997
## 856 1.6178367
## 336 0.8326684
## 743 0.8340276
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err.abs
## 749 1.8979274
## 858 1.8709997
## 856 1.6178367
## 336 0.8326684
## 743 0.8340276
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.center.scale.glmnet
## 749 2.569101
## 858 2.569054
## 856 2.495270
## 336 2.412139
## 743 2.480923
## startprice.log10.predict.RFE.X.center.scale.glmnet.err
## 749 1.8979286
## 858 1.8709533
## 856 1.5921797
## 336 0.8325845
## 743 0.8278068
## startprice.log10.predict.RFE.X.center.scale.glmnet.err.abs
## 749 1.8979286
## 858 1.8709533
## 856 1.5921797
## 336 0.8325845
## 743 0.8278068
## startprice.log10.predict.RFE.X.center.scale.glmnet.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.RFE.X.spatialSign.glmnet
## 749 2.557881
## 858 2.545282
## 856 2.388260
## 336 2.417292
## 743 2.456612
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err
## 749 1.8867085
## 858 1.8471813
## 856 1.4851695
## 336 0.8377374
## 743 0.8034956
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err.abs
## 749 1.8867085
## 858 1.8471813
## 856 1.4851695
## 336 0.8377374
## 743 0.8034956
## startprice.log10.predict.RFE.X.spatialSign.glmnet.accurate
## 749 FALSE
## 858 FALSE
## 856 FALSE
## 336 FALSE
## 743 FALSE
## startprice.log10.predict.Ensemble.glmnet
## 749 NA
## 858 NA
## 856 NA
## 336 NA
## 743 NA
## startprice.log10.predict.Ensemble.glmnet.err
## 749 NA
## 858 NA
## 856 NA
## 336 NA
## 743 NA
## startprice.log10.predict.Ensemble.glmnet.err.abs
## 749 NA
## 858 NA
## 856 NA
## 336 NA
## 743 NA
## startprice.log10.predict.Ensemble.glmnet.accurate
## 749 NA
## 858 NA
## 856 NA
## 336 NA
## 743 NA
## startprice.log10.predict.Final.Ensemble.glmnet
## 749 2.731972
## 858 2.727863
## 856 2.349224
## 336 2.368655
## 743 2.378893
## startprice.log10.predict.Final.Ensemble.glmnet.err
## 749 2.0607993
## 858 2.0297623
## 856 1.4461342
## 336 0.7891005
## 743 0.7257769
## startprice.log10.predict.Final.Ensemble.glmnet.err.abs
## 749 2.0607993
## 858 2.0297623
## 856 1.4461342
## 336 0.7891005
## 743 0.7257769
## startprice.log10.predict.Final.Ensemble.glmnet.accurate .label
## 749 FALSE 11448
## 858 FALSE 11583
## 856 FALSE 11581
## 336 FALSE 10837
## 743 FALSE 11442
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "startprice.log10.predict.Final.Ensemble.glmnet"
## [2] "startprice.log10.predict.Final.Ensemble.glmnet.err"
## [3] "startprice.log10.predict.Final.Ensemble.glmnet.err.abs"
## [4] "startprice.log10.predict.Final.Ensemble.glmnet.accurate"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 15 fit.data.training 8 1 1 358.713 368.421
## 16 predict.data.new 9 0 0 368.422 NA
## elapsed
## 15 9.708
## 16 NAStep 9.0: predict data new
# Compute final model predictions
#glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); all.equal(sav_trnobs_df, glb_trnobs_df); all.equal(sav_newobs_df, glb_newobs_df)
if (glb_is_classification && glb_is_binomial)
prob_threshold_def <-
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "opt.prob.threshold.OOB"] else
prob_threshold_def <- NULL
for (obsSet in c("trn", "new")) {
obs_df <- switch(obsSet, all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
obs_df <- glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id,
rsp_var_out = glb_rsp_var_out, prob_threshold_def = prob_threshold_def)
if (obsSet == "all") glb_allobs_df <- obs_df else
if (obsSet == "trn") glb_trnobs_df <- obs_df else
if (obsSet == "new") glb_newobs_df <- obs_df
}
rm(obs_df)
glb_allobs_df <- orderBy(reformulate(glb_id_var), myrbind_df(glb_trnobs_df, glb_newobs_df))
glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id = glb_fin_mdl_id,
prob_threshold = prob_threshold_def)## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 13
## UniqueID description startprice condition cellular carrier color
## 866 11591 2.99 New Unknown Unknown Unknown
## 745 11444 3.99 New Unknown Unknown Unknown
## 1329 12479 5.65 New Unknown Unknown Unknown
## 1331 12481 7.99 New Unknown Unknown Unknown
## 1112 12096 9.00 Used Unknown Unknown Space Gray
## storage productline .src .grpid startprice.log10 .rnorm
## 866 Unknown Unknown Test <NA> 0.4756712 0.23582653
## 745 Unknown Unknown Test <NA> 0.6009729 0.08115265
## 1329 Unknown Unknown Test <NA> 0.7520484 -2.22233585
## 1331 Unknown Unknown Test <NA> 0.9025468 0.74009665
## 1112 64 iPad Air 2 Test <NA> 0.9542425 -1.27788921
## startprice.log10.cut.fctr descr.my condition.fctr cellular.fctr
## 866 (0.473,1.32] New Unknown
## 745 (0.473,1.32] New Unknown
## 1329 (0.473,1.32] New Unknown
## 1331 (0.473,1.32] New Unknown
## 1112 (0.473,1.32] Used Unknown
## carrier.fctr color.fctr storage.fctr D.terms.post.stop.n
## 866 Unknown Unknown Unknown 0
## 745 Unknown Unknown Unknown 0
## 1329 Unknown Unknown Unknown 0
## 1331 Unknown Unknown Unknown 0
## 1112 Unknown Space Gray 64 0
## D.terms.post.stop.n.log D.weight.post.stop.sum D.terms.post.stem.n
## 866 0 0 0
## 745 0 0 0
## 1329 0 0 0
## 1331 0 0 0
## 1112 0 0 0
## D.terms.post.stem.n.log D.weight.post.stem.sum
## 866 0 0
## 745 0 0
## 1329 0 0
## 1331 0 0
## 1112 0 0
## D.weight.sum.stem.stop.Ratio D.T.condit D.T.use D.T.scratch
## 866 1 0 0 0
## 745 1 0 0 0
## 1329 1 0 0 0
## 1331 1 0 0 0
## 1112 1 0 0 0
## D.T.screen D.wrds.n.log D.wrds.unq.n.log D.weight.sum
## 866 0 0 0 0
## 745 0 0 0 0
## 1329 0 0 0 0
## 1331 0 0 0 0
## 1112 0 0 0 0
## D.ratio.weight.sum.wrds.n D.chrs.n.log D.chrs.uppr.n.log
## 866 0 0 0
## 745 0 0 0
## 1329 0 0 0
## 1331 0 0 0
## 1112 0 0 0
## D.chrs.pnct11.n.log D.chrs.pnct13.n.log D.wrds.stop.n.log
## 866 0 0 0
## 745 0 0 0
## 1329 0 0 0
## 1331 0 0 0
## 1112 0 0 0
## D.ratio.wrds.stop.n.wrds.n prdl.descr.my.fctr startprice.dgt1.is9
## 866 1 Unknown#0 0
## 745 1 Unknown#0 0
## 1329 1 Unknown#0 0
## 1331 1 Unknown#0 0
## 1112 1 iPadAir2#0 1
## startprice.dgt2.is9 startprice.dcm1.is9 startprice.dcm2.is9
## 866 0 1 1
## 745 0 1 1
## 1329 0 0 0
## 1331 0 1 1
## 1112 0 0 0
## .clusterid .clusterid.fctr .lcn sold
## 866 1 1 OOB 0
## 745 1 1 OOB 0
## 1329 1 1 OOB NA
## 1331 1 1 OOB NA
## 1112 1 1 OOB NA
## startprice.log10.predict.RFE.X.glm
## 866 2.534750
## 745 2.534485
## 1329 2.547161
## 1331 2.535613
## 1112 2.707869
## startprice.log10.predict.RFE.X.glm.err
## 866 2.059079
## 745 1.933512
## 1329 1.795112
## 1331 1.633066
## 1112 1.753627
## startprice.log10.predict.RFE.X.glm.err.abs
## 866 2.059079
## 745 1.933512
## 1329 1.795112
## 1331 1.633066
## 1112 1.753627
## startprice.log10.predict.RFE.X.glm.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 866 2.569054
## 745 2.569054
## 1329 2.569101
## 1331 2.569054
## 1112 2.652730
## startprice.log10.predict.RFE.X.glmnet.err
## 866 2.093383
## 745 1.968081
## 1329 1.817053
## 1331 1.666507
## 1112 1.698488
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 866 2.093383
## 745 1.968081
## 1329 1.817053
## 1331 1.666507
## 1112 1.698488
## startprice.log10.predict.RFE.X.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.rpart
## 866 2.594138
## 745 2.594138
## 1329 2.594138
## 1331 2.594138
## 1112 2.426605
## startprice.log10.predict.RFE.X.rpart.err
## 866 2.118467
## 745 1.993165
## 1329 1.842090
## 1331 1.691592
## 1112 1.472363
## startprice.log10.predict.RFE.X.rpart.err.abs
## 866 2.118467
## 745 1.993165
## 1329 1.842090
## 1331 1.691592
## 1112 1.472363
## startprice.log10.predict.RFE.X.rpart.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.gbm
## 866 2.660779
## 745 2.660779
## 1329 2.653162
## 1331 2.660779
## 1112 2.743275
## startprice.log10.predict.RFE.X.gbm.err
## 866 2.185108
## 745 2.059806
## 1329 1.901113
## 1331 1.758232
## 1112 1.789032
## startprice.log10.predict.RFE.X.gbm.err.abs
## 866 2.185108
## 745 2.059806
## 1329 1.901113
## 1331 1.758232
## 1112 1.789032
## startprice.log10.predict.RFE.X.gbm.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.rf
## 866 2.709043
## 745 2.709043
## 1329 2.710873
## 1331 2.709043
## 1112 2.709937
## startprice.log10.predict.RFE.X.rf.err
## 866 2.233372
## 745 2.108071
## 1329 1.958825
## 1331 1.806497
## 1112 1.755694
## startprice.log10.predict.RFE.X.rf.err.abs
## 866 2.233372
## 745 2.108071
## 1329 1.958825
## 1331 1.806497
## 1112 1.755694
## startprice.log10.predict.RFE.X.rf.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 866 2.554886
## 745 2.554886
## 1329 2.552102
## 1331 2.554886
## 1112 2.610182
## startprice.log10.predict.RFE.X.svmLinear.err
## 866 2.079215
## 745 1.953913
## 1329 1.800053
## 1331 1.652339
## 1112 1.655940
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 866 2.079215
## 745 1.953913
## 1329 1.800053
## 1331 1.652339
## 1112 1.655940
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 866 2.555239
## 745 2.555239
## 1329 2.558916
## 1331 2.555239
## 1112 2.607258
## startprice.log10.predict.RFE.X.svmLinear2.err
## 866 2.079568
## 745 1.954266
## 1329 1.806868
## 1331 1.652692
## 1112 1.653015
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 866 2.079568
## 745 1.954266
## 1329 1.806868
## 1331 1.652692
## 1112 1.653015
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.earth
## 866 2.611742
## 745 2.611742
## 1329 2.611742
## 1331 2.611742
## 1112 2.646351
## startprice.log10.predict.RFE.X.earth.err
## 866 2.136071
## 745 2.010769
## 1329 1.859694
## 1331 1.709195
## 1112 1.692108
## startprice.log10.predict.RFE.X.earth.err.abs
## 866 2.136071
## 745 2.010769
## 1329 1.859694
## 1331 1.709195
## 1112 1.692108
## startprice.log10.predict.RFE.X.earth.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 866 2.576492
## 745 2.576492
## 1329 2.587724
## 1331 2.576492
## 1112 2.696330
## startprice.log10.predict.RFE.X.bagEarth.err
## 866 2.100821
## 745 1.975519
## 1329 1.835676
## 1331 1.673946
## 1112 1.742087
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 866 2.100821
## 745 1.975519
## 1329 1.835676
## 1331 1.673946
## 1112 1.742087
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.Interact.glmnet
## 866 2.549609
## 745 2.549609
## 1329 2.549609
## 1331 2.549609
## 1112 2.631891
## startprice.log10.predict.RFE.X.Interact.glmnet.err
## 866 2.073937
## 745 1.948636
## 1329 1.797560
## 1331 1.647062
## 1112 1.677649
## startprice.log10.predict.RFE.X.Interact.glmnet.err.abs
## 866 2.073937
## 745 1.948636
## 1329 1.797560
## 1331 1.647062
## 1112 1.677649
## startprice.log10.predict.RFE.X.Interact.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet
## 866 2.569100
## 745 2.569100
## 1329 2.569100
## 1331 2.569100
## 1112 2.654657
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err
## 866 2.093429
## 745 1.968127
## 1329 1.817052
## 1331 1.666554
## 1112 1.700414
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.err.abs
## 866 2.093429
## 745 1.968127
## 1329 1.817052
## 1331 1.666554
## 1112 1.700414
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.center.scale.glmnet
## 866 2.569054
## 745 2.569054
## 1329 2.569101
## 1331 2.569054
## 1112 2.652730
## startprice.log10.predict.RFE.X.center.scale.glmnet.err
## 866 2.093383
## 745 1.968081
## 1329 1.817053
## 1331 1.666507
## 1112 1.698488
## startprice.log10.predict.RFE.X.center.scale.glmnet.err.abs
## 866 2.093383
## 745 1.968081
## 1329 1.817053
## 1331 1.666507
## 1112 1.698488
## startprice.log10.predict.RFE.X.center.scale.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.RFE.X.spatialSign.glmnet
## 866 2.554010
## 745 2.554141
## 1329 2.550995
## 1331 2.552723
## 1112 2.603319
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err
## 866 2.078339
## 745 1.953168
## 1329 1.798947
## 1331 1.650176
## 1112 1.649077
## startprice.log10.predict.RFE.X.spatialSign.glmnet.err.abs
## 866 2.078339
## 745 1.953168
## 1329 1.798947
## 1331 1.650176
## 1112 1.649077
## startprice.log10.predict.RFE.X.spatialSign.glmnet.accurate
## 866 FALSE
## 745 FALSE
## 1329 FALSE
## 1331 FALSE
## 1112 FALSE
## startprice.log10.predict.Final.Ensemble.glmnet
## 866 2.728493
## 745 2.728447
## 1329 2.733336
## 1331 2.728642
## 1112 2.776731
## startprice.log10.predict.Final.Ensemble.glmnet.err
## 866 2.252822
## 745 2.127474
## 1329 1.981288
## 1331 1.826096
## 1112 1.822489
## startprice.log10.predict.Final.Ensemble.glmnet.err.abs
## 866 2.252822
## 745 2.127474
## 1329 1.981288
## 1331 1.826096
## 1112 1.822489
## startprice.log10.predict.Final.Ensemble.glmnet.accurate .label
## 866 FALSE 11591
## 745 FALSE 11444
## 1329 FALSE 12479
## 1331 FALSE 12481
## 1112 FALSE 12096
if (is.null(glb_out_obs)) obs_df <- glb_newobs_df else
obs_df <- switch(glb_out_obs,
all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11932, 11943,
12050, 12115, 12171,
12253, 12285, 12367, 12388, 12399,
12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11982, 11990, 11991, 11994, 11999,
12000, 12002, 12004, 12018, 12021, 12065, 12072,
12111, 12114, 12126, 12134, 12152, 12172,
12213, 12214, 12233, 12265, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
for (obs_id in glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y") &
(glb_newobs_df[ , "startprice"] > 675), "UniqueID"]) {
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
} else {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id))]
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text=feat))]
}
}
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 > 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 < 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
# if (nrow(tmp_newobs_df) > 0)
# stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
tmp_newobs_df <- cbind(glb_newobs_df, submit_df[, "Probability1", FALSE])
# if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
# (tmp_newobs_df[, "Probability1"] >= 0.5), "startprice"]) >
# max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
# (glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
# stop("startprice for some +ve predictions > 675")
# Check predictions that are outside of data ranges
#stop(here")
tmp_feats_df <- subset(glb_feats_df,
!nzv &
(exclude.as.feat != 1) &
!grepl(".fctr", id, fixed=TRUE))[, "id", FALSE]
ranges_all_df <- glb_allobs_df[, tmp_feats_df$id] %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather() %>%
dplyr::mutate(id=str_sub(key, 1, -5),
stat=str_sub(key, -3)) %>%
dplyr::select(-key) %>%
tidyr::spread(stat, value)
# sav_ranges_trn_df <- ranges_trn_df; all.equal(sav_ranges_trn_df, ranges_trn_df)
# sav_ranges_new_df <- ranges_new_df; all.equal(sav_ranges_new_df, ranges_new_df)
get_ranges_df <- function(obs_df, feats, class_var) {
ranges_df <- obs_df[, c(class_var, feats)] %>%
dplyr::group_by_(class_var) %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather(key, value, -1) %>%
mutate(id=str_sub(key, 1, -5),
stat.vname=paste0(str_sub(key, -3), ".", class_var)) %>%
unite_("stat.class", c("stat.vname", class_var), sep=".") %>%
dplyr::select(-key) %>%
spread(stat.class, value)
return(ranges_df)
}
rsp_var_out_OOB <- paste0(glb_rsp_var_out, glb_sel_mdl_id)
rsp_var_out_new <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
ranges_trn_df <- get_ranges_df(obs_df=glb_trnobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_fit_df <- get_ranges_df(obs_df=glb_fitobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_OOB_df <- get_ranges_df(obs_df=glb_OOBobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_OOB)
ranges_new_df <- get_ranges_df(obs_df=glb_newobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_new)
for (obsset in c("OOB", "new")) {
if (obsset == "OOB") {
ranges_ref_df <- ranges_fit_df; obs_df <- glb_OOBobs_df;
rsp_var_out_obs <- rsp_var_out_OOB; sprintf_pfx <- "OOBobs";
} else {
ranges_ref_df <- ranges_trn_df; obs_df <- glb_newobs_df;
rsp_var_out_obs <- rsp_var_out_new; sprintf_pfx <- "newobs";
}
plt_feats_df <- glb_feats_df %>%
merge(ranges_all_df, all=TRUE) %>%
merge(ranges_ref_df, all=TRUE) %>%
merge(ranges_OOB_df, all=TRUE) %>%
merge(ranges_new_df, all=TRUE) %>%
subset(!is.na(min) & (id != ".rnorm"))
row.names(plt_feats_df) <- plt_feats_df$id
range_outlier_ids <- c(NULL)
for (clss in unique(obs_df[, rsp_var_out_obs])) {
for (stat in c("min", "max")) {
if (stat == "min") {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("min", rsp_var_out_obs, clss, sep=".")] <
plt_feats_df[, paste("min", glb_rsp_var, clss, sep=".")]), "id"]
} else {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("max", rsp_var_out_obs, clss, sep=".")] >
plt_feats_df[, paste("max", glb_rsp_var, clss, sep=".")]), "id"]
}
if (length(dsp_feats) > 0) {
ths_ids <- c(NULL)
for (feat in dsp_feats) {
if (stat == "min") {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] <
plt_feats_df[plt_feats_df$id == feat, paste("min", glb_rsp_var, clss, sep=".")]),
glb_id_var])
} else {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] >
plt_feats_df[plt_feats_df$id == feat, paste("max", glb_rsp_var, clss, sep=".")]),
glb_id_var])
}
}
tmp_obs_df <- obs_df[obs_df[, glb_id_var] %in% ths_ids,
c(glb_id_var, rsp_var_out_obs, dsp_feats)]
if (stat == "min") {
print(sprintf("%s %s %s: min < min of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
} else {
print(sprintf("%s %s %s: max > max of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
}
myprint_df(tmp_obs_df)
print(subset(plt_feats_df, id %in% dsp_feats))
range_outlier_ids <- union(range_outlier_ids, ths_ids)
}
}
}
print(sprintf("%s total range outliers: %d", sprintf_pfx, length(range_outlier_ids)))
}
}
out_fname <- paste0(glb_out_pfx, "out.csv")
write.csv(obsout_df, out_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words term weights for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
terms_mtrx <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(terms_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(terms_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#terms_mtrx[2442, terms_mtrx[2442, ] > 0]
print(sprintf(" All post-stem-words term freq distribution for %s:", txt_var))
print(table(glb_post_stem_words_terms_df_lst[[txt_var]]$freq))
print(sprintf(" All post-stem-words term length distribution for %s:", txt_var))
print(table(nchar(glb_post_stem_words_terms_df_lst[[txt_var]]$term)))
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], nchar(term) >= 10))
print(sprintf(" Analyzed term weights for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
anl_terms_vctr <- union(select_terms, assoc_terms)
print(subset(tmp_df, term %in% anl_terms_vctr))
# tmp_freq1_df <- subset(tmp_df, freq == 1)
# tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
# print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## condit condit 125.47296 335 30 0.03580843 0.03580843 104.69372
## use use 95.56784 169 153 0.02167331 0.02167331 76.15142
## new new 86.04035 100 92 0.05227226 0.05227226 73.78521
## scratch scratch 79.97880 180 124 0.01416983 0.01416983 64.15626
## good good 77.72398 130 59 -0.02086895 0.02086895 55.50939
## work work 75.40786 124 160 -0.20145080 0.20145080 51.88958
## weight.(1.32,2.16] weight.(0.473,1.32]
## condit 20.47912 0.3001182
## use 19.41641 0.0000000
## new 12.25514 0.0000000
## scratch 15.39439 0.4281416
## good 21.71938 0.4952109
## work 21.75096 1.7673240
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## cosmet cosmet 52.220995 92 32 0.04847578 0.04847578 43.681190
## lock lock 32.141461 18 77 -0.10622132 0.10622132 5.208321
## list list 20.272753 35 76 0.03836131 0.03836131 13.985903
## order order 14.558753 14 100 -0.01888349 0.01888349 11.644882
## restor restor 12.349224 9 122 -0.05042551 0.05042551 8.994720
## full full 9.473837 7 54 -0.22722034 0.22722034 4.352844
## weight.(1.32,2.16] weight.(0.473,1.32]
## cosmet 8.539805 0
## lock 26.933140 0
## list 6.286851 0
## order 2.913872 0
## restor 3.354505 0
## full 5.120993 0
## term weight freq pos cor.y cor.y.abs weight.(2.16,3]
## regular regular 4.025712 5 119 0.018625060 0.018625060 3.209021
## left left 4.006747 3 73 0.006360932 0.006360932 4.006747
## sever sever 3.547737 4 130 -0.063207924 0.063207924 2.486631
## miss miss 3.442698 4 88 -0.053428356 0.053428356 2.499493
## els els 2.992694 3 46 -0.105866734 0.105866734 0.000000
## mean mean 2.967961 3 83 0.014328655 0.014328655 2.967961
## weight.(1.32,2.16] weight.(0.473,1.32]
## regular 0.8166916 0
## left 0.0000000 0
## sever 1.0611055 0
## miss 0.9432049 0
## els 2.9926936 0
## mean 0.0000000 0
## UniqueID
## 155 10287
## 502 11111
## 1019 11866
## descr.my
## 155
## 502
## 1019 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/ 9 out of 10 - Will
## [1] 0
## [1] " All post-stem-words term freq distribution for descr.my:"
##
## 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21
## 8 15 15 15 6 5 6 3 3 4 4 4 2 5 1 5 4 4
## 22 23 24 25 27 28 29 30 31 32 33 35 36 37 40 43 44 45
## 1 2 2 3 1 1 3 2 2 1 1 1 1 1 1 1 1 2
## 46 47 49 52 53 58 60 62 66 68 71 72 83 85 86 92 100 124
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## 127 130 169 180 335
## 1 1 1 1 1
## [1] " All post-stem-words term length distribution for descr.my:"
##
## 2 3 4 5 6 7 8 9 10
## 3 20 46 40 29 11 7 3 1
## term weight freq pos cor.y cor.y.abs
## shrinkwrap shrinkwrap 5.032672 4 132 0.07554534 0.07554534
## weight.(2.16,3] weight.(1.32,2.16] weight.(0.473,1.32]
## shrinkwrap 5.032672 0 0
## [1] " Analyzed term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs
## condit condit 125.47296 335 30 0.035808430 0.035808430
## use use 95.56784 169 153 0.021673307 0.021673307
## new new 86.04035 100 92 0.052272261 0.052272261
## scratch scratch 79.97880 180 124 0.014169828 0.014169828
## good good 77.72398 130 59 -0.020868951 0.020868951
## work work 75.40786 124 160 -0.201450795 0.201450795
## screen screen 68.23607 127 125 0.009868377 0.009868377
## great great 63.37884 71 61 -0.143630845 0.143630845
## box box 59.96162 72 20 -0.032314024 0.032314024
## no no 55.91465 83 93 -0.035402395 0.035402395
## excel excel 54.93234 53 49 0.068583842 0.068583842
## like like 54.59488 49 75 0.049828794 0.049828794
## item item 53.33620 85 68 0.081696680 0.081696680
## cosmet cosmet 52.22100 92 32 0.048475782 0.048475782
## minor minor 47.62803 86 86 0.015531088 0.015531088
## crack crack 41.51390 40 34 -0.038806922 0.038806922
## perfect perfect 40.05461 36 108 0.019681283 0.019681283
## fulli fulli 39.84689 52 55 -0.068700204 0.068700204
## function function 39.80531 60 56 -0.058406757 0.058406757
## hous hous 37.19398 71 64 0.030457274 0.030457274
## origin origin 34.72008 46 101 0.033856890 0.033856890
## will will 33.59663 31 159 0.059021676 0.059021676
## 100 100 31.82867 58 2 0.006410486 0.006410486
## damag damag 23.79180 17 35 -0.066566054 0.066566054
## open open 23.75362 18 99 -0.030101292 0.030101292
## tab tab 20.97673 27 142 -0.006360116 0.006360116
## list list 20.27275 35 76 0.038361305 0.038361305
## affect affect 19.61869 24 7 0.005925489 0.005925489
## weight.(2.16,3] weight.(1.32,2.16] weight.(0.473,1.32]
## condit 104.69372 20.479123 0.3001182
## use 76.15142 19.416415 0.0000000
## new 73.78521 12.255137 0.0000000
## scratch 64.15626 15.394394 0.4281416
## good 55.50939 21.719376 0.4952109
## work 51.88958 21.750964 1.7673240
## screen 43.08176 25.154304 0.0000000
## great 52.19017 9.019124 2.1695486
## box 55.80552 4.156103 0.0000000
## no 46.30420 9.610454 0.0000000
## excel 52.92973 2.002611 0.0000000
## like 46.16185 8.433026 0.0000000
## item 44.13300 9.203200 0.0000000
## cosmet 43.68119 8.539805 0.0000000
## minor 37.31814 10.309895 0.0000000
## crack 22.98476 18.529136 0.0000000
## perfect 37.66110 2.393514 0.0000000
## fulli 27.69329 12.153600 0.0000000
## function 26.48475 13.320559 0.0000000
## hous 26.65962 9.914493 0.6198710
## origin 31.96159 2.758490 0.0000000
## will 30.31538 3.281256 0.0000000
## 100 24.94268 6.885987 0.0000000
## damag 21.44463 2.347173 0.0000000
## open 21.27119 2.482433 0.0000000
## tab 17.63192 3.344808 0.0000000
## list 13.98590 6.286851 0.0000000
## affect 15.12050 4.498196 0.0000000if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Ensemble.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.Ensemble.glmnet"get_dsp_models_df()## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.lm Max.cor.Y.rcv.1X1.glmnet
## 0 0
## Max.cor.Y.rcv.1X1.cp.0.rpart RFE.X.bagEarth
## 0 1## id min.RMSE.fit
## Ensemble.glmnet Ensemble.glmnet 0.05431488
## RFE.X.YeoJohnson.glmnet RFE.X.YeoJohnson.glmnet 0.11195463
## RFE.X.glmnet RFE.X.glmnet 0.11333769
## RFE.X.center.scale.glmnet RFE.X.center.scale.glmnet 0.11333769
## Low.cor.X.glmnet Low.cor.X.glmnet 0.11358324
## All.X.glmnet All.X.glmnet 0.11367253
## RFE.X.spatialSign.glmnet RFE.X.spatialSign.glmnet 0.11739606
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet 0.11824441
## RFE.X.svmLinear2 RFE.X.svmLinear2 0.12784799
## RFE.X.svmLinear RFE.X.svmLinear 0.12975144
## RFE.X.earth RFE.X.earth 0.13344779
## RFE.X.rf RFE.X.rf 0.13393978
## RFE.X.bagEarth RFE.X.bagEarth 0.15072587
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet 0.15408481
## RFE.X.glm RFE.X.glm 0.15424250
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart 0.16010585
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet 0.16776207
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet 0.16867136
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet 0.16941231
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet 0.17411210
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet 0.17430070
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet 0.17595782
## RFE.X.rpart RFE.X.rpart 0.17702373
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet 0.17938212
## RFE.X.gbm RFE.X.gbm 0.20068953
## Max.cor.Y.rpart Max.cor.Y.rpart 0.21849527
## MFO.lm MFO.lm 0.28009850
## max.Adj.R.sq.fit max.R.sq.fit
## Ensemble.glmnet 0.963776640 0.966096363
## RFE.X.YeoJohnson.glmnet 0.630609328 0.894459808
## RFE.X.glmnet 0.633685895 0.895338827
## RFE.X.center.scale.glmnet 0.633685895 0.895338827
## Low.cor.X.glmnet 0.673135106 0.895338827
## All.X.glmnet 0.627259332 0.895338827
## RFE.X.spatialSign.glmnet 0.643074137 0.898021182
## RFE.X.Interact.glmnet 1.038887873 0.912837527
## RFE.X.svmLinear2 0.869318344 0.883480888
## RFE.X.svmLinear 0.861588243 0.876588532
## RFE.X.earth 0.346497046 0.810065644
## RFE.X.rf 0.817193139 0.946868942
## RFE.X.bagEarth 0.672945619 0.904944786
## Max.cor.Y.rcv.1X1.glmnet 0.665315477 0.698289322
## RFE.X.glm 0.868667919 0.910073107
## Max.cor.Y.rcv.1X1.cp.0.rpart NA 0.674249249
## Max.cor.Y.rcv.5X1.glmnet 0.658160088 0.691838897
## Max.cor.Y.rcv.5X3.glmnet 0.658160088 0.691838897
## Max.cor.Y.rcv.5X5.glmnet 0.658160088 0.691838897
## Max.cor.Y.rcv.3X3.glmnet 0.658160088 0.691838897
## Max.cor.Y.rcv.3X5.glmnet 0.658160088 0.691838897
## Max.cor.Y.rcv.3X1.glmnet 0.658160088 0.691838897
## RFE.X.rpart NA 0.661480554
## Interact.High.cor.Y.glmnet 0.430616677 0.708296228
## RFE.X.gbm 0.533226681 0.864336819
## Max.cor.Y.rpart NA 0.513541388
## MFO.lm -0.001929766 0.003005849if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
#stop(here"); glb_to_sav(); glb_ctgry_df <- sav_ctgry_df
# OOB_ctgry_df <- myget_category_stats(glb_OOBobs_df, glb_sel_mdl_id, "OOB")
# glb_ctgry_df <- merge(glb_ctgry_df, subset(OOB_ctgry_df, select=-.n.OOB),
# by=glb_category_var, all=TRUE)
#
# #glb_fitobs_df <- glb_get_predictions(glb_fitobs_df, glb_sel_mdl_id, glb_rsp_var_out)
# glb_ctgry_df <- merge(glb_ctgry_df,
# myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
# by=glb_category_var, all=TRUE)
# row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glb_model_evl_criteria)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
mdl_id=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}## [1] "Ensemble.glmnet OOB RMSE: 0.2884"
## prdl.descr.my.fctr .n.Tst .n.OOB.x .freqRatio.Tst
## Unknown#0 Unknown#0 83 86 0.068032787
## Unknown#1 Unknown#1 65 66 0.053278689
## iPadmini#1 iPadmini#1 63 64 0.051639344
## iPad1#1 iPad1#1 59 61 0.048360656
## iPad1#0 iPad1#0 57 57 0.046721311
## iPadAir2#1 iPadAir2#1 29 30 0.023770492
## iPadmini2#1 iPadmini2#1 24 24 0.019672131
## iPadAir2#0 iPadAir2#0 92 92 0.075409836
## iPadmini2#0 iPadmini2#0 44 44 0.036065574
## iPadmini#0 iPadmini#0 97 97 0.079508197
## iPadmini3#1 iPadmini3#1 12 12 0.009836066
## iPad2#0 iPad2#0 72 73 0.059016393
## iPadmini3#0 iPadmini3#0 63 63 0.051639344
## iPad3#1 iPad3#1 63 64 0.051639344
## iPad4#1 iPad4#1 74 76 0.060655738
## iPadAir#0 iPadAir#0 60 60 0.049180328
## iPad4#0 iPad4#0 42 42 0.034426230
## iPad2#1 iPad2#1 126 126 0.103278689
## iPadAir#1 iPadAir#1 67 68 0.054918033
## iPad3#0 iPad3#0 28 28 0.022950820
## .freqRatio.OOB err.abs.fit.sum err.abs.fit.mean .n.fit
## Unknown#0 0.06974858 0.97148291 0.08095691 12
## Unknown#1 0.05352798 0.64173657 0.06417366 10
## iPadmini#1 0.05190592 0.61520329 0.05126694 12
## iPad1#1 0.04947283 0.63882356 0.04914027 13
## iPad1#0 0.04622871 0.59144797 0.04224628 14
## iPadAir2#1 0.02433090 0.15612627 0.03903157 4
## iPadmini2#1 0.01946472 0.14258652 0.03564663 4
## iPadAir2#0 0.07461476 0.52415447 0.03494363 15
## iPadmini2#0 0.03568532 0.38178240 0.03470749 11
## iPadmini#0 0.07866991 0.64386457 0.03219323 20
## iPadmini3#1 0.00973236 0.03093406 0.03093406 1
## iPad2#0 0.05920519 0.36039655 0.03003305 12
## iPadmini3#0 0.05109489 0.23487994 0.02935999 8
## iPad3#1 0.05190592 0.37686393 0.02898953 13
## iPad4#1 0.06163828 0.16843758 0.02807293 6
## iPadAir#0 0.04866180 0.33307290 0.02775607 12
## iPad4#0 0.03406326 0.21763133 0.01978467 11
## iPad2#1 0.10218978 0.30401382 0.01600073 19
## iPadAir#1 0.05515004 0.05829557 0.01457389 4
## iPad3#0 0.02270884 0.04210759 0.01403586 3
## err.abs.OOB.sum err.abs.OOB.mean .n.OOB.y err.abs.trn.sum
## Unknown#0 43.540313 0.50628271 86 5.85114494
## Unknown#1 16.541495 0.25062871 66 1.36751348
## iPadmini#1 7.657773 0.11965270 64 0.66358505
## iPad1#1 6.697294 0.10979170 61 0.80367007
## iPad1#0 7.642822 0.13408460 57 0.59144797
## iPadAir2#1 8.173194 0.27243980 30 0.39529323
## iPadmini2#1 2.674542 0.11143926 24 0.14258652
## iPadAir2#0 8.687314 0.09442733 92 0.52415447
## iPadmini2#0 4.568395 0.10382717 44 0.38178240
## iPadmini#0 12.842770 0.13239970 97 0.64386457
## iPadmini3#1 2.822667 0.23522228 12 0.03093406
## iPad2#0 11.101121 0.15207015 73 0.89061219
## iPadmini3#0 5.142928 0.08163377 63 0.23487994
## iPad3#1 6.225250 0.09726953 64 0.39633662
## iPad4#1 13.401229 0.17633196 76 1.77334504
## iPadAir#0 7.556450 0.12594083 60 0.33307290
## iPad4#0 5.826083 0.13871626 42 0.21763133
## iPad2#1 15.134406 0.12011434 126 0.30401382
## iPadAir#1 9.577808 0.14085012 68 0.06214445
## iPad3#0 5.081787 0.18149239 28 0.04210759
## err.abs.trn.mean .n.trn err.abs.new.sum err.abs.new.mean
## Unknown#0 0.39007633 15 38.660651 0.46579098
## Unknown#1 0.12431941 11 15.815718 0.24331873
## iPadmini#1 0.05104500 13 7.609391 0.12078399
## iPad1#1 0.05357800 15 6.532447 0.11071944
## iPad1#0 0.04224628 14 7.642822 0.13408460
## iPadAir2#1 0.07905865 5 7.934027 0.27358714
## iPadmini2#1 0.03564663 4 2.674542 0.11143926
## iPadAir2#0 0.03494363 15 8.687314 0.09442733
## iPadmini2#0 0.03470749 11 4.568395 0.10382717
## iPadmini#0 0.03219323 20 12.842770 0.13239970
## iPadmini3#1 0.03093406 1 2.822667 0.23522228
## iPad2#0 0.06850863 13 10.570906 0.14681813
## iPadmini3#0 0.02935999 8 5.142928 0.08163377
## iPad3#1 0.02830976 14 6.205777 0.09850440
## iPad4#1 0.22166813 8 11.796322 0.15940975
## iPadAir#0 0.02775607 12 7.556450 0.12594083
## iPad4#0 0.01978467 11 5.826083 0.13871626
## iPad2#1 0.01600073 19 15.134406 0.12011434
## iPadAir#1 0.01242889 5 9.573959 0.14289491
## iPad3#0 0.01403586 3 5.081787 0.18149239
## .n.new
## Unknown#0 83
## Unknown#1 65
## iPadmini#1 63
## iPad1#1 59
## iPad1#0 57
## iPadAir2#1 29
## iPadmini2#1 24
## iPadAir2#0 92
## iPadmini2#0 44
## iPadmini#0 97
## iPadmini3#1 12
## iPad2#0 72
## iPadmini3#0 63
## iPad3#1 63
## iPad4#1 74
## iPadAir#0 60
## iPad4#0 42
## iPad2#1 126
## iPadAir#1 67
## iPad3#0 28
## .n.Tst .n.OOB.x .freqRatio.Tst .freqRatio.OOB
## 1220.0000000 1233.0000000 1.0000000 1.0000000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 7.4338418 0.7038474 204.0000000 200.8956429
## err.abs.OOB.mean .n.OOB.y err.abs.trn.sum err.abs.trn.mean
## 3.2846153 1233.0000000 15.6501206 1.3466015
## .n.trn err.abs.new.sum err.abs.new.mean .n.new
## 217.0000000 192.6793641 3.2211254 1220.0000000
## [1] "Final.Ensemble.glmnet prediction stats for glb_newobs_df:"
## id max.R.sq.new min.RMSE.new max.Adj.R.sq.new
## 1 Final.Ensemble.glmnet 0.2556024 0.2724951 0.2475782if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(dsp_ctgry_df <- orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print("Top category OOB errors:")
print(glb_OOBobs_df[(glb_OOBobs_df[, glb_category_var] ==
dsp_ctgry_df[1, glb_category_var]) &
!glb_OOBobs_df[, predct_accurate_var_name],
c(glb_id_var, glb_rsp_var_raw, paste0(glb_rsp_var_out, glb_sel_mdl_id),
glb_category_var,
row.names(head(myget_feats_importance(glb_sel_mdl), 5)),
# "biddable", "startprice", "condition",
glb_txt_vars)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
if (!is.null(glb_featsimp_df))
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")),
subset(glb_featsimp_df, importance > 10)))## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.rf 100.00000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.77866
## startprice.log10.predict.RFE.X.glm 30.02773
## startprice.log10.predict.RFE.X.gbm 19.70305
## startprice.log10.predict.RFE.X.bagEarth 18.57133
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.55671
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.55671
## startprice.log10.predict.RFE.X.svmLinear2 18.55671
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.43573
## startprice.log10.predict.RFE.X.rpart 10.74382
## startprice.log10.predict.RFE.X.glmnet 10.66827
## importance
## startprice.log10.predict.RFE.X.rf 100.00000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.77866
## startprice.log10.predict.RFE.X.glm 30.02773
## startprice.log10.predict.RFE.X.gbm 19.70305
## startprice.log10.predict.RFE.X.bagEarth 18.57133
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.55671
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.55671
## startprice.log10.predict.RFE.X.svmLinear2 18.55671
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.43573
## startprice.log10.predict.RFE.X.rpart 10.74382
## startprice.log10.predict.RFE.X.glmnet 10.66827
## Final.Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.rf 100.00000
## startprice.log10.predict.RFE.X.Interact.glmnet 43.77866
## startprice.log10.predict.RFE.X.glm 30.02773
## startprice.log10.predict.RFE.X.gbm 19.70305
## startprice.log10.predict.RFE.X.bagEarth 18.57133
## startprice.log10.predict.RFE.X.center.scale.glmnet 18.55671
## startprice.log10.predict.RFE.X.spatialSign.glmnet 18.55671
## startprice.log10.predict.RFE.X.svmLinear2 18.55671
## startprice.log10.predict.RFE.X.YeoJohnson.glmnet 15.43573
## startprice.log10.predict.RFE.X.rpart 10.74382
## startprice.log10.predict.RFE.X.glmnet 10.66827print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
# Use this to see how prediction changes by changing one or more values
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
# dsp_chisq.test(Headline.contains="[Vi]deo")
if ((length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)) {
print(diff)
stop("glb_*obs_df not in sync")
}
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
sav_fin_mdl <- glb_fin_mdl; sav_sel_mdl <- glb_sel_mdl
save(sav_fin_mdl, sav_sel_mdl, file=paste0(glb_out_pfx, "sav_mdl.RData"))
# load(file=paste0(glb_out_pfx, "sav_mdl_01.RData"), verbose=TRUE)
# prv_fin_mdl <- sav_fin_mdl; prv_sel_mdl <- sav_sel_mdl
# load(file=paste0(glb_out_pfx, "sav_mdl.RData"), verbose=TRUE)
# cur_fin_mdl <- sav_fin_mdl; cur_sel_mdl <- sav_sel_mdl
# all.equal(cur_fin_mdl, prv_fin_mdl)
# cur_fitobs_df <- cur_fin_mdl$trainingData; prv_fitobs_df <- prv_fin_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# nrow(cur_fitobs_df); nrow(prv_fitobs_df)
# names(cur_fitobs_df); names(prv_fitobs_df)
# all.equal(cur_fin_mdl$bestTune, prv_fin_mdl$bestTune)
# all.equal(glb_sel_mdl, sav_sel_mdl)
# cur_fitobs_df <- cur_sel_mdl$trainingData; prv_fitobs_df <- prv_sel_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# head(myget_feats_importance(glb_sel_mdl)); head(myget_feats_importance(sav_sel_mdl))
# head(myget_feats_importance(cur_sel_mdl)); head(myget_feats_importance(prv_sel_mdl))
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 16 predict.data.new 9 0 0 368.422 414.536
## 17 display.session.info 10 0 0 414.537 NA
## elapsed
## 16 46.114
## 17 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 11 fit.models 7 1 1 158.014
## 12 fit.models 7 2 2 299.584
## 16 predict.data.new 9 0 0 368.422
## 10 fit.models 7 0 0 119.314
## 5 extract.features 3 0 0 45.715
## 9 select.features 6 0 0 99.700
## 1 import.data 1 0 0 10.311
## 2 inspect.data 2 0 0 25.603
## 7 manage.missing.data 4 1 1 81.216
## 15 fit.data.training 8 1 1 358.713
## 13 fit.models 7 3 3 351.636
## 3 scrub.data 2 1 1 39.633
## 8 partition.data.training 5 0 0 94.322
## 6 cluster.data 4 0 0 79.203
## 4 transform.data 2 2 2 45.046
## 14 fit.data.training 8 0 0 358.249
## end elapsed duration
## 11 299.584 141.570 141.570
## 12 351.635 52.051 52.051
## 16 414.536 46.114 46.114
## 10 158.013 38.699 38.699
## 5 79.203 33.488 33.488
## 9 119.314 19.614 19.614
## 1 25.603 15.292 15.292
## 2 39.633 14.030 14.030
## 7 94.321 13.106 13.105
## 15 368.421 9.708 9.708
## 13 358.248 6.612 6.612
## 3 45.046 5.413 5.413
## 8 99.699 5.377 5.377
## 6 81.216 2.013 2.013
## 4 45.715 0.669 0.669
## 14 358.713 0.464 0.464
## [1] "Total Elapsed Time: 414.536 secs"